@article{adrianiEvolutionFakeNews2019,
  title = {The {{Evolution}} of {{Fake News}} and the {{Abuse}} of {{Emerging Technologies}}},
  author = {Adriani, Roberto},
  year = 2019,
  month = apr,
  journal = {European Journal of Social Sciences},
  volume = {2},
  number = {1},
  pages = {18--24},
  issn = {2601-8640},
  doi = {10.26417/ejss-2019.v2i1-53},
  urldate = {2024-12-11},
  abstract = {Fake news and post-factual society are quite popular terms today. The literature is investigating this phenomenon from different perspectives. We also know the psychological dimension at the basis of fake news (Lynch M. 2016) and the debate around the need for a new media policy (Goodman E. 2017). However, something else is very important: the evolution process of fake news. Far from being a still life, fake news will evolve and this needs to be monitored closely. In a post-factual society fake news could be fuelled by the abuse of new powerful technologies (Murgia M. and Kuchler H. 2017).},
  langid = {english},
  keywords = {AI,fake news,post-truth},
  annotation = {Read\_Status: To Read\\
Read\_Status\_Date: 2025-07-09T09:29:05.379Z},
  file = {C:\Users\nasty\Documents\Zotero\Adriani - 2019 - The Evolution of Fake News and the Abuse of Emerging Technologies 1.pdf}
}

@misc{akhynkoHiddenPersuasionDetecting2025,
  title = {Hidden {{Persuasion}}: {{Detecting Manipulative Narratives}} on {{Social Media During}} the 2022 {{Russian Invasion}} of {{Ukraine}}},
  shorttitle = {Hidden {{Persuasion}}},
  author = {Akhynko, Kateryna and Kosovan, Oleksandr and Trokhymovych, Mykola},
  year = 2025,
  month = may,
  number = {arXiv:2505.24028},
  eprint = {2505.24028},
  primaryclass = {cs},
  publisher = {arXiv},
  doi = {10.48550/arXiv.2505.24028},
  urldate = {2025-07-01},
  abstract = {This paper presents one of the top-performing solutions to the UNLP 2025 Shared Task on Detecting Manipulation in Social Media. The task focuses on detecting and classifying rhetorical and stylistic manipulation techniques used to influence Ukrainian Telegram users. For the classification subtask, we fine-tuned the Gemma 2 language model with LoRA adapters and applied a second-level classifier leveraging meta-features and threshold optimization. For span detection, we employed an XLM-RoBERTa model trained for multi-target, including token binary classification. Our approach achieved 2nd place in classification and 3rd place in span detection.},
  archiveprefix = {arXiv},
  keywords = {icwsm2025},
  annotation = {Read\_Status: Read\\
Read\_Status\_Date: 2025-09-25T18:20:04.032Z},
  file = {C\:\\Users\\nasty\\Documents\\Zotero\\Akhynko et al. - 2025 - Hidden Persuasion Detecting Manipulative Narratives on Social Media During the 2022 Russian Invasio.pdf;C\:\\Users\\nasty\\Zotero\\storage\\7JIX5LCR\\Akhynko et al. - 2025 - Hidden Persuasion Detecting Manipulative Narratives on Social Media During the 2022 Russian Invasio.pdf;C\:\\Users\\nasty\\Zotero\\storage\\UYT9HVJA\\Akhynko et al. - 2025 - Hidden Persuasion Detecting Manipulative Narratives on Social Media During the 2022 Russian Invasio.pdf;C\:\\Users\\nasty\\Zotero\\storage\\AZNDXJQF\\2505.html}
}

@inproceedings{akhynkoHiddenPersuasionDetecting2025a,
  title = {Hidden {{Persuasion}}: {{Detecting Manipulative Narratives}} on {{Social Media During}} the 2022 {{Russian Invasion}} of {{Ukraine}}},
  shorttitle = {Hidden {{Persuasion}}},
  booktitle = {Proceedings of the {{Fourth Ukrainian Natural Language Processing Workshop}} ({{UNLP}} 2025)},
  author = {Akhynko, Kateryna and Kosovan, Oleksandr and Trokhymovych, Mykola},
  editor = {Romanyshyn, Mariana},
  year = 2025,
  month = jul,
  pages = {194--202},
  publisher = {Association for Computational Linguistics},
  address = {Vienna, Austria (online)},
  doi = {10.18653/v1/2025.unlp-1.19},
  urldate = {2025-12-09},
  abstract = {This paper presents one of the top-performing solutions to the UNLP 2025 Shared Task on Detecting Manipulation in Social Media. The task focuses on detecting and classifying rhetorical and stylistic manipulation techniques used to influence Ukrainian Telegram users. For the classification subtask, we fine-tuned the Gemma 2 language model with LoRA adapters and applied a second-level classifier leveraging meta-features and threshold optimization. For span detection, we employed an XLM-RoBERTa model trained for multi-target, including token binary classification. Our approach achieved 2nd place in classification and 3rd place in span detection.},
  isbn = {979-8-89176-269-5},
  annotation = {Read\_Status: New\\
Read\_Status\_Date: 2025-12-09T13:25:45.513Z},
  file = {C:\Users\nasty\Documents\Zotero\Akhynko et al. - 2025 - Hidden Persuasion Detecting Manipulative Narratives on Social Media During the 2022 Russian Invasio 1.pdf}
}

@inproceedings{alviPlagiarismDetectionTexts2017,
  title = {Plagiarism {{Detection}} in {{Texts Obfuscated}} with {{Homoglyphs}}},
  booktitle = {Advances in {{Information Retrieval}}},
  author = {Alvi, Faisal and Stevenson, Mark and Clough, Paul},
  editor = {Jose, Joemon M and Hauff, Claudia and Alt{\i}ngovde, Ismail Sengor and Song, Dawei and Albakour, Dyaa and Watt, Stuart and Tait, John},
  year = 2017,
  pages = {669--675},
  publisher = {Springer International Publishing},
  address = {Cham},
  doi = {10.1007/978-3-319-56608-5_64},
  abstract = {Homoglyphs can be used for disguising plagiarized text by replacing letters in source texts with visually identical letters from other scripts. Most current plagiarism detection systems are not able to detect plagiarism when text has been obfuscated using homoglyphs. In this work, we present two alternative approaches for detecting plagiarism in homoglyph obfuscated texts. The first approach utilizes the Unicode list of confusables to replace homoglyphs with visually identical letters, while the second approach uses a similarity score computed using normalized hamming distance to match homoglyph obfuscated words with source words. Empirical testing on datasets from PAN-2015 shows that both approaches perform equally well for plagiarism detection in homoglyph obfuscated texts.},
  isbn = {978-3-319-56608-5},
  langid = {english},
  keywords = {Approximate String Match,Identical Letter,Source Document,Source Word,String Similarity},
  annotation = {Read\_Status: Not Reading\\
Read\_Status\_Date: 2025-07-28T11:33:07.720Z},
  file = {C:\Users\User\OneDrive - Universität des Saarlandes\Documents\Zotero\Alvi et al. - 2017 - Plagiarism Detection in Texts Obfuscated with Homoglyphs.pdf}
}

@incollection{Alyukov2024,
  title = {Confuse and {{Normalise}}: {{Authoritarian Propaganda}} in a {{High-Choice Media Environment}} and {{Russia}}'s {{Invasion}} of {{Ukraine}}},
  booktitle = {Russian Propaganda Today: {{Challenges}}, Effectiveness, and Resistance},
  author = {Alyukov, Maxim and Kunilovskaya, Maria and Semenov, Andrei},
  editor = {Goode, Paul},
  year = 2025,
  pages = {in print},
  publisher = {University of Michigan press, University of Manchester Press},
  annotation = {Read\_Status: Read\\
Read\_Status\_Date: 2025-07-03T12:44:55.294Z},
  url = {https://www.researchgate.net/publication/392125201\_Confuse\_and\_Normalise\_Authoritarian\_Propaganda\_in\_a\_High-Choice\_Media\_Environment\_during\_Russia's\_Invasion\_of\_Ukraine},
  file = {C\:\\Users\\nasty\\Documents\\Zotero\\Alyukov et al. - 2024 - Confuse and Normalise Authoritarian Propaganda in a High-Choice Media Environment and Russia’s Inva.pdf;C\:\\Users\\nasty\\Documents\\Zotero\\Alyukov et al. - 2024 - Confuse and Normalise Authoritarian Propaganda in a High-Choice Media Environment and Russia’s Inva.pdf}
}

@misc{alyukovConfuseNormaliseAuthoritarian2025,
  title = {Confuse and {{Normalise}}: {{Authoritarian Propaganda}} in a {{High-Choice Media Environment}} during {{Russia}}'s {{Invasion}} of {{Ukraine}}},
  shorttitle = {Confuse and {{Normalise}}},
  author = {Alyukov, Maxim and Kunilovskaya, Maria and Semenov, Andrei},
  year = 2025,
  journal = {ResearchGate},
  urldate = {2025-07-07},
  abstract = {Access 135+ million publications and connect with 20+ million researchers. Join for free and gain visibility by uploading your research.},
  url = {https://www.researchgate.net/publication/392125201\_Confuse\_and\_Normalise\_Authoritarian\_Propaganda\_in\_a\_High-Choice\_Media\_Environment\_during\_Russia's\_Invasion\_of\_Ukraine},
  langid = {english},
  annotation = {Read\_Status: Read\\
Read\_Status\_Date: 2025-07-28T15:29:42.383Z},
  file = {C:\Users\nasty\Zotero\storage\E9W5G5EE\392125201_Confuse_and_Normalise_Authoritarian_Propaganda_in_a_High-Choice_Media_Environment_dur.html}
}

@article{alyukovPropagandaSetbacksAppropriation2022,
  title = {Propaganda {{Setbacks}} and {{Appropriation}} of {{Anti-war}} Language: ``{{Special Military Operation}}'' in {{Russian Mass Media}} and {{Social Networks}} ({{February-July}} 2022). {{Monitoring Report}} \textnumero 1},
  shorttitle = {Propaganda {{Setbacks}} and {{Appropriation}} of {{Anti-war}} Language},
  author = {Alyukov, Maxim and Semenov, Andrei and Kunilovskaya, Maria},
  year = 2022,
  month = aug,
  publisher = {Russian Election Monitor},
  urldate = {2025-12-08},
  langid = {english},
  annotation = {Read\_Status: New\\
Read\_Status\_Date: 2025-12-08T15:20:23.519Z},
  file = {C\:\\Users\\nasty\\Documents\\Zotero\\Alyukov et al. - 2022 - Propaganda Setbacks and Appropriation of Anti-war language “Special Military Operation” in Russian.pdf;C\:\\Users\\nasty\\Zotero\\storage\\5SKKXTIT\\propaganda-setbacks-and-appropriation-of-anti-war-language-specia.html}
}

@inproceedings{alyukovWartimeMediaMonitor2023,
  title = {Wartime {{Media Monitor}} ({{WarMM-2022}}): {{A Study}} of {{Information Manipulation}} on {{Russian Social Media}} during the {{Russia-Ukraine War}}},
  shorttitle = {Wartime {{Media Monitor}} ({{WarMM-2022}})},
  booktitle = {Proceedings of the 7th {{Joint SIGHUM Workshop}} on {{Computational Linguistics}} for {{Cultural Heritage}}, {{Social Sciences}}, {{Humanities}} and {{Literature}}},
  author = {Alyukov, Maxim and Kunilovskaya, Maria and Semenov, Andrei},
  editor = {{Degaetano-Ortlieb}, Stefania and Kazantseva, Anna and Reiter, Nils and Szpakowicz, Stan},
  year = 2023,
  month = may,
  pages = {152--161},
  publisher = {Association for Computational Linguistics},
  address = {Dubrovnik, Croatia},
  doi = {10.18653/v1/2023.latechclfl-1.17},
  urldate = {2024-10-08},
  abstract = {This study relies on natural language processing to explore the nature of online communication in Russia during the war on Ukraine in 2022. The analysis of a large corpus of publications in traditional media and on social media identifies massive state interventions aimed at manipulating public opinion. The study relies on expertise in media studies and political science to trace the major themes and strategies of the propagandist narratives on three major Russian social media platforms over several months as well as their perception by the users. Distributions of several keyworded pro-war and anti-war topics are examined to reveal the cross-platform specificity of social media audiences. We release WarMM-2022, a 1.7M posts corpus. This corpus includes publications related to the Russia-Ukraine war, which appeared in Russian mass media and on social networks between February and September 2022. The corpus can be useful for the development of NLP approaches to propaganda detection and subsequent studies of propaganda campaigns in social sciences in addition to traditional methods, such as content analysis, focus groups, surveys, and experiments.},
  annotation = {Read\_Status: Read\\
Read\_Status\_Date: 2025-07-03T12:45:00.116Z},
  file = {C\:\\Users\\nasty\\Documents\\Zotero\\Alyukov et al. - 2023 - Wartime Media Monitor (WarMM-2022) A Study of Information Manipulation on Russian Social Media duri 1.pdf;C\:\\Users\\nasty\\Documents\\Zotero\\Alyukov et al. - 2023 - Wartime Media Monitor (WarMM-2022) A Study of Information Manipulation on Russian Social Media duri 1.pdf}
}

@article{aroraDetectingHarmfulContent2024,
  title = {Detecting {{Harmful Content}} on {{Online Platforms}}: {{What Platforms Need}} vs. {{Where Research Efforts Go}}},
  shorttitle = {Detecting {{Harmful Content}} on {{Online Platforms}}},
  author = {Arora, Arnav and Nakov, Preslav and Hardalov, Momchil and Sarwar, Sheikh Muhammad and Nayak, Vibha and Dinkov, Yoan and Zlatkova, Dimitrina and Dent, Kyle and Bhatawdekar, Ameya and Bouchard, Guillaume and Augenstein, Isabelle},
  year = 2024,
  month = mar,
  journal = {ACM Computing Surveys},
  volume = {56},
  number = {3},
  pages = {1--17},
  issn = {0360-0300, 1557-7341},
  doi = {10.1145/3603399},
  urldate = {2024-11-21},
  abstract = {The proliferation of harmful content on online platforms is a major societal problem, which comes in many different forms, including hate speech, offensive language, bullying and harassment, misinformation, spam, violence, graphic content, sexual abuse, self-harm, and many others. Online platforms seek to moderate such content to limit societal harm, to comply with legislation, and to create a more inclusive environment for their users. Researchers have developed different methods for automatically detecting harmful content, often focusing on specific sub-problems or on narrow communities, as what is considered harmful often depends on the platform and on the context. We argue that there is currently a dichotomy between what types of harmful content online platforms seek to curb, and what research efforts there are to automatically detect such content. We thus survey existing methods as well as content moderation policies by online platforms in this light and suggest directions for future work.},
  langid = {english},
  annotation = {Read\_Status: Not Reading\\
Read\_Status\_Date: 2025-10-29T11:43:01.602Z},
  file = {C:\Users\nasty\Documents\Zotero\Arora et al. - 2024 - Detecting Harmful Content on Online Platforms What Platforms Need vs. Where Research Efforts Go 1.pdf}
}

@misc{augensteinExplainableFactChecking2021,
  title = {Towards {{Explainable Fact Checking}}},
  author = {Augenstein, Isabelle},
  year = 2021,
  month = dec,
  number = {arXiv:2108.10274},
  eprint = {2108.10274},
  publisher = {arXiv},
  urldate = {2024-11-21},
  abstract = {The past decade has seen a substantial rise in the amount of mis- and disinformation online, from targeted disinformation campaigns to influence politics, to the unintentional spreading of misinformation about public health. This development has spurred research in the area of automatic fact checking, from approaches to detect check-worthy claims and determining the stance of tweets towards claims, to methods to determine the veracity of claims given evidence documents. These automatic methods are often content-based, using natural language processing methods, which in turn utilise deep neural networks to learn higher-order features from text in order to make predictions. As deep neural networks are black-box models, their inner workings cannot be easily explained. At the same time, it is desirable to explain how they arrive at certain decisions, especially if they are to be used for decision making. While this has been known for some time, the issues this raises have been exacerbated by models increasing in size, and by EU legislation requiring models to be used for decision making to provide explanations, and, very recently, by legislation requiring online platforms operating in the EU to provide transparent reporting on their services. Despite this, current solutions for explainability are still lacking in the area of fact checking. This thesis presents my research on automatic fact checking, including claim check-worthiness detection, stance detection and veracity prediction. Its contributions go beyond fact checking, with the thesis proposing more general machine learning solutions for natural language processing in the area of learning with limited labelled data. Finally, the thesis presents some first solutions for explainable fact checking.},
  archiveprefix = {arXiv},
  keywords = {Computer Science - Computation and Language,Statistics - Machine Learning},
  annotation = {Read\_Status: Not Reading\\
Read\_Status\_Date: 2025-07-28T09:04:49.233Z},
  file = {C\:\\Users\\nasty\\Documents\\Zotero\\Augenstein - 2021 - Towards Explainable Fact Checking 1.pdf;C\:\\Users\\nasty\\Zotero\\storage\\KRD6KALD\\2108.html}
}

@misc{augensteinStanceDetectionBidirectional2016,
  title = {Stance {{Detection}} with {{Bidirectional Conditional Encoding}}},
  author = {Augenstein, Isabelle and Rockt{\"a}schel, Tim and Vlachos, Andreas and Bontcheva, Kalina},
  year = 2016,
  month = sep,
  number = {arXiv:1606.05464},
  eprint = {1606.05464},
  publisher = {arXiv},
  doi = {10.48550/arXiv.1606.05464},
  urldate = {2024-11-21},
  abstract = {Stance detection is the task of classifying the attitude expressed in a text towards a target such as Hillary Clinton to be "positive", negative" or "neutral". Previous work has assumed that either the target is mentioned in the text or that training data for every target is given. This paper considers the more challenging version of this task, where targets are not always mentioned and no training data is available for the test targets. We experiment with conditional LSTM encoding, which builds a representation of the tweet that is dependent on the target, and demonstrate that it outperforms encoding the tweet and the target independently. Performance is improved further when the conditional model is augmented with bidirectional encoding. We evaluate our approach on the SemEval 2016 Task 6 Twitter Stance Detection corpus achieving performance second best only to a system trained on semi-automatically labelled tweets for the test target. When such weak supervision is added, our approach achieves state-of-the-art results.},
  archiveprefix = {arXiv},
  keywords = {Computer Science - Computation and Language,Computer Science - Machine Learning,Computer Science - Neural and Evolutionary Computing},
  annotation = {Read\_Status: Not Reading\\
Read\_Status\_Date: 2025-08-21T12:21:59.776Z},
  file = {C\:\\Users\\nasty\\Documents\\Zotero\\Augenstein et al. - 2016 - Stance Detection with Bidirectional Conditional Encoding.pdf;C\:\\Users\\nasty\\Zotero\\storage\\PE3M32BI\\1606.html}
}

@inproceedings{augensteinUSFDSemEval2016Task2016,
  title = {{{USFD}} at {{SemEval-2016 Task}} 6: {{Any-Target Stance Detection}} on {{Twitter}} with {{Autoencoders}}},
  shorttitle = {{{USFD}} at {{SemEval-2016 Task}} 6},
  booktitle = {Proceedings of the 10th {{International Workshop}} on {{Semantic Evaluation}} ({{SemEval-2016}})},
  author = {Augenstein, Isabelle and Vlachos, Andreas and Bontcheva, Kalina},
  editor = {Bethard, Steven and Carpuat, Marine and Cer, Daniel and Jurgens, David and Nakov, Preslav and Zesch, Torsten},
  year = 2016,
  month = jun,
  pages = {389--393},
  publisher = {Association for Computational Linguistics},
  address = {San Diego, California},
  doi = {10.18653/v1/S16-1063},
  urldate = {2024-11-21},
  annotation = {Read\_Status: Not Reading\\
Read\_Status\_Date: 2025-07-28T11:34:24.149Z},
  file = {C:\Users\nasty\Documents\Zotero\Augenstein et al. - 2016 - USFD at SemEval-2016 Task 6 Any-Target Stance Detection on Twitter with Autoencoders.pdf}
}

@inproceedings{azarbonyadWordsAreMalleable2017,
  title = {Words Are {{Malleable}}: {{Computing Semantic Shifts}} in {{Political}} and {{Media Discourse}}},
  shorttitle = {Words Are {{Malleable}}},
  booktitle = {Proceedings of the 2017 {{ACM}} on {{Conference}} on {{Information}} and {{Knowledge Management}}},
  author = {Azarbonyad, Hosein and Dehghani, Mostafa and Beelen, Kaspar and Arkut, Alexandra and Marx, Maarten and Kamps, Jaap},
  year = 2017,
  month = nov,
  series = {{{CIKM}} '17},
  pages = {1509--1518},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  doi = {10.1145/3132847.3132878},
  urldate = {2024-11-21},
  abstract = {Recently, researchers started to pay attention to the detection of temporal shifts in the meaning of words. However, most (if not all) of these approaches restricted their efforts to uncovering change over time, thus neglecting other valuable dimensions such as social or political variability. We propose an approach for detecting semantic shifts between different viewpoints---broadly defined as a set of texts that share a specific metadata feature, which can be a time-period, but also a social entity such as a political party. For each viewpoint, we learn a semantic space in which each word is represented as a low dimensional neural embedded vector. The challenge is to compare the meaning of a word in one space to its meaning in another space and measure the size of the semantic shifts. We compare the effectiveness of a measure based on optimal transformations between the two spaces with a measure based on the similarity of the neighbors of the word in the respective spaces. Our experiments demonstrate that the combination of these two performs best. We show that the semantic shifts not only occur over time but also along different viewpoints in a short period of time. For evaluation, we demonstrate how this approach captures meaningful semantic shifts and can help improve other tasks such as the contrastive viewpoint summarization and ideology detection (measured as classification accuracy) in political texts. We also show that the two laws of semantic change which were empirically shown to hold for temporal shifts also hold for shifts across viewpoints. These laws state that frequent words are less likely to shift meaning while words with many senses are more likely to do so.},
  isbn = {978-1-4503-4918-5},
  annotation = {Read\_Status: Read\\
Read\_Status\_Date: 2025-07-09T09:32:22.842Z},
  file = {C\:\\Users\\nasty\\Documents\\Zotero\\Azarbonyad et al. - 2017 - Words are Malleable Computing Semantic Shifts in Political and Media Discourse 1.pdf;C\:\\Users\\nasty\\Documents\\Zotero\\Azarbonyad et al. - 2017 - Words are Malleable Computing Semantic Shifts in Political and Media Discourse 1.pdf}
}

@inproceedings{baesMultidimensionalFrameworkEvaluating2024,
  title = {A {{Multidimensional Framework}} for {{Evaluating Lexical Semantic Change}} with {{Social Science Applications}}},
  booktitle = {Proceedings of the 62nd {{Annual Meeting}} of the {{Association}} for {{Computational Linguistics}} ({{Volume}} 1: {{Long Papers}})},
  author = {Baes, Naomi and Haslam, Nick and Vylomova, Ekaterina},
  editor = {Ku, Lun-Wei and Martins, Andre and Srikumar, Vivek},
  year = 2024,
  month = aug,
  pages = {1390--1415},
  publisher = {Association for Computational Linguistics},
  address = {Bangkok, Thailand},
  doi = {10.18653/v1/2024.acl-long.76},
  urldate = {2025-07-28},
  abstract = {Historical linguists have identified multiple forms of lexical semantic change. We present a three-dimensional framework for integrating these forms and a unified computational methodology for evaluating them concurrently. The dimensions represent increases or decreases in semantic 1) sentiment (valence of a target word's collocates), 2) intensity (emotional arousal of collocates or the frequency of intensifiers), and 3) breadth (diversity of contexts in which the target word appears). These dimensions can be complemented by evaluation of shifts in the frequency of the target words and the thematic content of its collocates. This framework enables lexical semantic change to be mapped economically and systematically and has applications in computational social science. We present an illustrative analysis of semantic shifts in mental health and mental illness in two corpora, demonstrating patterns of semantic change that illuminate contemporary concerns about pathologization, stigma, and concept creep.},
  annotation = {Read\_Status: To Read\\
Read\_Status\_Date: 2025-07-28T11:37:45.425Z},
  file = {C:\Users\nasty\Documents\Zotero\Baes et al. - 2024 - A Multidimensional Framework for Evaluating Lexical Semantic Change with Social Science Applications.pdf}
}

@article{barron-cedenoProppyOrganizingNews2019,
  title = {Proppy: {{Organizing}} the News Based on Their Propagandistic Content},
  shorttitle = {Proppy},
  author = {{Barr{\'o}n-Cede{\~n}o}, Alberto and Jaradat, Israa and Da San Martino, Giovanni and Nakov, Preslav},
  year = 2019,
  month = sep,
  journal = {Information Processing \& Management},
  volume = {56},
  number = {5},
  pages = {1849--1864},
  issn = {0306-4573},
  doi = {10.1016/j.ipm.2019.03.005},
  urldate = {2025-08-29},
  abstract = {Propaganda is a mechanism to influence public opinion, which is inherently present in extremely biased and fake news. Here, we propose a model to automatically assess the level of propagandistic content in an article based on different representations, from writing style and readability level to the presence of certain keywords. We experiment thoroughly with different variations of such a model on a new publicly available corpus, and we show that character n-grams and other style features outperform existing alternatives to identify propaganda based on word n-grams. Unlike previous work, we make sure that the test data comes from news sources that were unseen on training, thus penalizing learning algorithms that model the news sources used at training time as opposed to solving the actual task. We integrate our supervised model in a public website, which organizes recent articles covering the same event on the basis of their propagandistic contents. This allows users to quickly explore different perspectives of the same story, and it also enables investigative journalists to dig further into how different media use stories and propaganda to pursue their agenda.},
  annotation = {Read\_Status: To Read\\
Read\_Status\_Date: 2025-08-29T15:22:58.075Z},
  file = {C\:\\Users\\nasty\\Documents\\Zotero\\Barrón-Cedeño et al. - 2019 - Proppy Organizing the news based on their propagandistic content.pdf;C\:\\Users\\nasty\\Zotero\\storage\\4MLZ4QIZ\\S0306457318306058.html}
}

@article{barron-cedenoProppySystemUnmask2019,
  title = {Proppy: {{A System}} to {{Unmask Propaganda}} in {{Online News}}},
  shorttitle = {Proppy},
  author = {{Barr{\'o}n-Cede{\~n}o}, Alberto and Martino, Giovanni Da San and Jaradat, Israa and Nakov, Preslav},
  year = 2019,
  month = jul,
  journal = {Proceedings of the AAAI Conference on Artificial Intelligence},
  volume = {33},
  number = {01},
  pages = {9847--9848},
  issn = {2374-3468},
  doi = {10.1609/aaai.v33i01.33019847},
  urldate = {2025-08-05},
  abstract = {We present proppy, the first publicly available real-world, real-time propaganda detection system for online news, which aims at raising awareness, thus potentially limiting the impact of propaganda and helping fight disinformation. The system constantly monitors a number of news sources, deduplicates and clusters the news into events, and organizes the articles about an event on the basis of the likelihood that they contain propagandistic content. The system is trained on known propaganda sources using a variety of stylistic features. The evaluation results on a standard dataset show stateof-the-art results for propaganda detection.},
  copyright = {Copyright (c) 2019 Association for the Advancement of Artificial Intelligence},
  langid = {english},
  annotation = {Read\_Status: To Read\\
Read\_Status\_Date: 2025-08-20T11:12:37.005Z},
  file = {C:\Users\nasty\Documents\Zotero\Barrón-Cedeño et al. - 2019 - Proppy A System to Unmask Propaganda in Online News.pdf}
}

@article{benjaminHybridForecastingGeopolitical2023,
  title = {Hybrid Forecasting of Geopolitical Events},
  author = {Benjamin, Daniel M. and Morstatter, Fred and Abbas, Ali E. and Abeliuk, Andres and Atanasov, Pavel and Bennett, Stephen and Beger, Andreas and Birari, Saurabh and Budescu, David V. and Catasta, Michele and Ferrara, Emilio and Haravitch, Lucas and Himmelstein, Mark and Hossain, KSM Tozammel and Huang, Yuzhong and Jin, Woojeong and Joseph, Regina and Leskovec, Jure and Matsui, Akira and Mirtaheri, Mehrnoosh and Ren, Xiang and Satyukov, Gleb and Sethi, Rajiv and Singh, Amandeep and Sosic, Rok and Steyvers, Mark and Szekely, Pedro A and Ward, Michael D. and Galstyan, Aram},
  year = 2023,
  journal = {AI Magazine},
  volume = {44},
  number = {1},
  pages = {112--128},
  issn = {2371-9621},
  doi = {10.1002/aaai.12085},
  urldate = {2025-01-29},
  abstract = {Sound decision-making relies on accurate prediction for tangible outcomes ranging from military conflict to disease outbreaks. To improve crowdsourced forecasting accuracy, we developed SAGE, a hybrid forecasting system that combines human and machine generated forecasts. The system provides a platform where users can interact with machine models and thus anchor their judgments on an objective benchmark. The system also aggregates human and machine forecasts weighting both for propinquity and based on assessed skill while adjusting for overconfidence. We present results from the Hybrid Forecasting Competition (HFC)---larger than comparable forecasting tournaments---including 1085 users forecasting 398 real-world forecasting problems over 8 months. Our main result is that the hybrid system generated more accurate forecasts compared to a human-only baseline, which had no machine generated predictions. We found that skilled forecasters who had access to machine-generated forecasts outperformed those who only viewed historical data. We also demonstrated the inclusion of machine-generated forecasts in our aggregation algorithms improved performance, both in terms of accuracy and scalability. This suggests that hybrid forecasting systems, which potentially require fewer human resources, can be a viable approach for maintaining a competitive level of accuracy over a larger number of forecasting questions.},
  copyright = {\copyright{} 2023 The Authors. AI Magazine published by Wiley Periodicals LLC on behalf of the Association for the Advancement of Artificial Intelligence.},
  langid = {english},
  annotation = {Read\_Status: Not Reading\\
Read\_Status\_Date: 2025-07-28T11:39:11.736Z},
  file = {C:\Users\nasty\Documents\Zotero\Benjamin et al. - 2023 - Hybrid forecasting of geopolitical events.pdf}
}

@inproceedings{bizzoniDiffusionScientificTerms2021,
  title = {The Diffusion of Scientific Terms -- Tracing Individuals' Influence in the History of Science for {{English}}},
  booktitle = {Proceedings of the 5th {{Joint SIGHUM Workshop}} on {{Computational Linguistics}} for {{Cultural Heritage}}, {{Social Sciences}}, {{Humanities}} and {{Literature}}},
  author = {Bizzoni, Yuri and {Degaetano-Ortlieb}, Stefania and Menzel, Katrin and Teich, Elke},
  editor = {{Degaetano-Ortlieb}, Stefania and Kazantseva, Anna and Reiter, Nils and Szpakowicz, Stan},
  year = 2021,
  month = nov,
  pages = {120--127},
  publisher = {Association for Computational Linguistics},
  address = {Punta Cana, Dominican Republic (online)},
  doi = {10.18653/v1/2021.latechclfl-1.14},
  urldate = {2025-07-23},
  abstract = {Tracing the influence of individuals or groups in social networks is an increasingly popular task in sociolinguistic studies. While methods to determine someone's influence in shortterm contexts (e.g., social media, on-line political debates) are widespread, influence in longterm contexts is less investigated and may be harder to capture. We study the diffusion of scientific terms in an English diachronic scientific corpus, applying Hawkes Processes to capture the role of individual scientists as ``influencers'' or ``influencees'' in the diffusion of new concepts. Our findings on two major scientific discoveries in chemistry and astronomy of the 18th century reveal that modelling both the introduction and diffusion of scientific terms in a historical corpus as Hawkes Processes allows detecting patterns of influence between authors on a long-term scale.},
  keywords = {cascade_reading_group},
  annotation = {Read\_Status: Read\\
Read\_Status\_Date: 2025-07-29T16:35:39.035Z},
  file = {C:\Users\nasty\Documents\Zotero\Bizzoni et al. - 2021 - The diffusion of scientific terms – tracing individuals' influence in the history of science for Eng 1.pdf}
}

@inproceedings{bizzoniGrammarMeaningAnalysing2019,
  title = {Grammar and {{Meaning}}: {{Analysing}} the {{Topology}} of {{Diachronic Word Embeddings}}},
  shorttitle = {Grammar and {{Meaning}}},
  booktitle = {Proceedings of the 1st {{International Workshop}} on {{Computational Approaches}} to {{Historical Language Change}}},
  author = {Bizzoni, Yuri and {Degaetano-Ortlieb}, Stefania and Menzel, Katrin and Krielke, Pauline and Teich, Elke},
  editor = {Tahmasebi, Nina and Borin, Lars and Jatowt, Adam and Xu, Yang},
  year = 2019,
  month = aug,
  pages = {175--185},
  publisher = {Association for Computational Linguistics},
  address = {Florence, Italy},
  doi = {10.18653/v1/W19-4722},
  urldate = {2025-08-08},
  abstract = {The paper showcases the application of word embeddings to change in language use in the domain of science, focusing on the Late Modern English period (17-19th century). Historically, this is the period in which many registers of English developed, including the language of science. Our overarching interest is the linguistic development of scientific writing to a distinctive (group of) register(s). A register is marked not only by the choice of lexical words (discourse domain) but crucially by grammatical choices which indicate style. The focus of the paper is on the latter, tracing words with primarily grammatical functions (function words and some selected, poly-functional word forms) diachronically. To this end, we combine diachronic word embeddings with appropriate visualization and exploratory techniques such as clustering and relative entropy for meaningful aggregation of data and diachronic comparison.},
  annotation = {Read\_Status: To Read\\
Read\_Status\_Date: 2025-08-20T13:14:15.733Z},
  file = {C:\Users\nasty\Documents\Zotero\Bizzoni et al. - 2019 - Grammar and Meaning Analysing the Topology of Diachronic Word Embeddings.pdf}
}

@article{bizzoniLinguisticVariationChange2020,
  title = {Linguistic {{Variation}} and {{Change}} in 250 {{Years}} of {{English Scientific Writing}}: {{A Data-Driven Approach}}},
  shorttitle = {Linguistic {{Variation}} and {{Change}} in 250 {{Years}} of {{English Scientific Writing}}},
  author = {Bizzoni, Yuri and {Degaetano-Ortlieb}, Stefania and Fankhauser, Peter and Teich, Elke},
  year = 2020,
  month = sep,
  journal = {Frontiers in Artificial Intelligence},
  volume = {3},
  pages = {73},
  issn = {2624-8212},
  doi = {10.3389/frai.2020.00073},
  urldate = {2025-03-12},
  abstract = {We trace the evolution of Scientific English through the Late Modern period to modern time on the basis of a comprehensive corpus composed of the Transactions and Proceedings of the Royal Society of London, the first and longest-running English scientific journal established in 1665. Specifically, we explore the linguistic imprints of specialization and diversification in the science domain which accumulate in the formation of ``scientific language'' and field-specific sublanguages/registers (chemistry, biology etc.). We pursue an exploratory, data-driven approach using state-of-the-art computational language models and combine them with selected information-theoretic measures (entropy, relative entropy) for comparing models along relevant dimensions of variation (time, register). Focusing on selected linguistic variables (lexis, grammar), we show how we deploy computational language models for capturing linguistic variation and change and discuss benefits and limitations.},
  langid = {english},
  keywords = {Cognitive balance,computational language models,Diachronic linguistic,diachronic variation in language use,evolution of Scientific English,linguistic change,Linguistic change,register variation,Scientific English,scientific evolu},
  annotation = {Read\_Status: Read\\
Read\_Status\_Date: 2025-07-28T11:48:01.964Z},
  file = {C\:\\Users\\nasty\\Documents\\Zotero\\Bizzoni et al. - 2020 - Linguistic Variation and Change in 250 Years of English Scientific Writing A Data-Driven Approach 2.pdf;C\:\\Users\\nasty\\Documents\\Zotero\\Bizzoni et al. - 2020 - Linguistic Variation and Change in 250 Years of English Scientific Writing A Data-Driven Approach 3.pdf;C\:\\Users\\nasty\\Documents\\Zotero\\Bizzoni et al. - 2020 - Linguistic Variation and Change in 250 Years of English Scientific Writing A Data-Driven Approach 4.pdf;C\:\\Users\\nasty\\Documents\\Zotero\\Bizzoni et al. - 2020 - Linguistic Variation and Change in 250 Years of English Scientific Writing A Data-Driven Approach.pdf;C\:\\Users\\nasty\\Documents\\Zotero\\Bizzoni et al. - 2020 - Linguistic Variation and Change in 250 Years of English Scientific Writing A Data-Driven Approach.pdf;C\:\\Users\\User\\OneDrive - Universität des Saarlandes\\Documents\\Zotero\\Bizzoni et al. - 2020 - Linguistic Variation and Change in 250 Years of English Scientific Writing A Data-Driven Approach 1.pdf}
}

@inproceedings{bizzoniStepsGenerationDiachronic2019,
  title = {Some Steps towards the Generation of Diachronic {{WordNets}}},
  booktitle = {Proceedings of the 22nd {{Nordic Conference}} on {{Computational Linguistics}}},
  author = {Bizzoni, Yuri and Mosbach, Marius and Klakow, Dietrich and {Degaetano-Ortlieb}, Stefania},
  editor = {Hartmann, Mareike and Plank, Barbara},
  year = 2019,
  month = sep,
  pages = {55--64},
  publisher = {Link\"oping University Electronic Press},
  address = {Turku, Finland},
  urldate = {2024-10-01},
  abstract = {We apply hyperbolic embeddings to trace the dynamics of change of conceptual-semantic relationships in a large diachronic scientific corpus (200 years). Our focus is on emerging scientific fields and the increasingly specialized terminology establishing around them. Reproducing high-quality hierarchical structures such as WordNet on a diachronic scale is a very difficult task. Hyperbolic embeddings can map partial graphs into low dimensional, continuous hierarchical spaces, making more explicit the latent structure of the input. We show that starting from simple lists of word pairs (rather than a list of entities with directional links) it is possible to build diachronic hierarchical semantic spaces which allow us to model a process towards specialization for selected scientific fields.},
  annotation = {Read\_Status: To Read\\
Read\_Status\_Date: 2025-08-25T15:20:11.787Z},
  file = {C\:\\Users\\nasty\\Documents\\Zotero\\Bizzoni et al. - 2019 - Some steps towards the generation of diachronic WordNets 1.pdf;C\:\\Users\\nasty\\Documents\\Zotero\\Bizzoni et al. - 2019 - Some steps towards the generation of diachronic WordNets 1.pdf;C\:\\Users\\nasty\\Documents\\Zotero\\Bizzoni et al. - 2019 - Some steps towards the generation of diachronic WordNets 2.pdf}
}

@article{bochkarevUniversalsHistoricalContingencies2014,
  title = {{Universals versus Historical Contingencies in Lexical Evolution}},
  author = {Bochkarev, Vladimir and Solovyev, Valery D. and Wichmann, Søren},
  year = {2014},
  month = dec,
  journal = {Journal of The Royal Society Interface},
  volume = {11},
  number = {101},
  pages = {20140841},
  publisher = {Royal Society},
  doi = {10.1098/rsif.2014.0841},
  urldate = {2024-12-10},
  abstract = {The frequency with which we use different words changes all the time, and every so often, a new lexical item is invented or another one ceases to be used. Beyond a small sample of lexical items whose properties are well studied, little is known about the dynamics of lexical evolution. How do the lexical inventories of languages, viewed as entire systems, evolve? Is the rate of evolution of the lexicon contingent upon historical factors or is it driven by regularities, perhaps to do with universals of cognition and social interaction? We address these questions using the Google Books N-Gram Corpus as a source of data and relative entropy as a measure of changes in the frequency distributions of words. It turns out that there are both universals and historical contingencies at work. Across several languages, we observe similar rates of change, but only at timescales of at least around five decades. At shorter timescales, the rate of change is highly variable and differs between languages. Major societal transformations as well as catastrophic events such as wars lead to increased change in frequency distributions, whereas stability in society has a dampening effect on lexical evolution.},
  keywords = {Google Books N-grams,Kullback-Leibler divergence,language dynamics,lexical change},
  file = {C:\Users\nasty\Zotero\storage\HYVY7IGE\Bochkarev et al. - 2014 - Universals versus historical contingencies in lexical evolution.pdf}
}

@article{bollaIdeaLiberty160018002020,
  title = {The {{Idea}} of {{Liberty}}, 1600-1800: {{A Distributional Concept Analysis}}},
  shorttitle = {The {{Idea}} of {{Liberty}}, 1600-1800},
  author = {de Bolla, Peter and Jones, Ewan and Nulty, Paul and Recchia, Gabriel and Regan, John},
  year = 2020,
  journal = {Journal of the History of Ideas},
  volume = {81},
  number = {3},
  pages = {381--406},
  issn = {0022-5037},
  doi = {10.1353/jhi.2020.0023},
  abstract = {This article uses computational and statistical methods for analyzing the concept of liberty 1600-1800. Based on a bespoke set of tools for parsing conceptual structures it contributes to the literature on the concept of liberty and engages with the thesis concerning negative liberty first put forward by Isaiah Berlin and subsequently modified by Quentin Skinner.},
  langid = {english},
  pmid = {33416754},
  keywords = {camp3_helsinki},
  annotation = {Read\_Status: Read\\
Read\_Status\_Date: 2025-08-08T14:04:24.072Z},
  file = {C\:\\Users\\nasty\\Documents\\Zotero\\Bolla et al. - 2020 - The Idea of Liberty, 1600-1800 A Distributional Concept Analysis 1.pdf;C\:\\Users\\nasty\\Documents\\Zotero\\Bolla et al. - 2020 - The Idea of Liberty, 1600-1800 A Distributional Concept Analysis.pdf}
}

@inproceedings{bonafiliaSuddenSemanticShifts2023,
  title = {Sudden {{Semantic Shifts}} in {{Swedish NATO}} Discourse},
  booktitle = {Proceedings of the 61st {{Annual Meeting}} of the {{Association}} for {{Computational Linguistics}} ({{Volume}} 4: {{Student Research Workshop}})},
  author = {Bonafilia, Brian and Bruinsma, Bastiaan and Saynova, Denitsa and Johansson, Moa},
  editor = {Padmakumar, Vishakh and Vallejo, Gisela and Fu, Yao},
  year = 2023,
  month = jul,
  pages = {184--193},
  publisher = {Association for Computational Linguistics},
  address = {Toronto, Canada},
  doi = {10.18653/v1/2023.acl-srw.28},
  urldate = {2025-01-23},
  abstract = {In this paper, we investigate a type of semantic shift that occurs when a sudden event radically changes public opinion on a topic. Looking at Sweden`s decision to apply for NATO membership in 2022, we use word embeddings to study how the associations users on Twitter have regarding NATO evolve. We identify several changes that we successfully validate against real-world events. However, the low engagement of the public with the issue often made it challenging to distinguish true signals from noise. We thus find that domain knowledge and data selection are of prime importance when using word embeddings to study semantic shifts.},
  annotation = {Read\_Status: To Read\\
Read\_Status\_Date: 2025-07-28T15:54:24.763Z},
  file = {C:\Users\nasty\Documents\Zotero\Bonafilia et al. - 2023 - Sudden Semantic Shifts in Swedish NATO discourse.pdf}
}

@article{brauschMachineLearningHistory2023,
  title = {Machine Learning for the History of Ideas},
  author = {Brausch, Simon and Gra{\ss}hoff, Gerd},
  year = 2023,
  journal = {Future Humanities},
  volume = {1},
  number = {1},
  pages = {e6},
  issn = {2770-2030},
  doi = {10.1002/fhu2.6},
  urldate = {2025-07-28},
  abstract = {The information technological progress that has been achieved over the last decades has also given the humanities the opportunity to expand their methodological toolbox. This paper explores how recent advancements in natural language processing may be used for research in the history of ideas so as to overcome traditional scholarship's inevitably selective approach to historical sources. By employing two machine learning techniques whose potential for the analysis of conceptual continuities and innovations has never been considered before, we aim to determine the extent to which they can enhance conventional research methods. It will amount to a critical evaluation of how the advantages of computational in-breadth analysis could be combined with the merits of traditional in-depth analysis in a philosophically fruitful way. After a brief technical description, the approach will be applied to an example: the conceptual (dis)continuity between medieval and early modern philosophy. All the challenges encountered during development and application will be carefully evaluated. We will then be able to assess whether these tools and techniques present promising extensions to the methodological toolbox of traditional scholarship, or whether they do not yet have the potential for a task as complex as the analysis of philosophical literature. The present investigation can thus be seen as an experiment on how far one can go with current machine-learning techniques in this area of research. In doing so, it provides important insights and guidance for future advances in the field.},
  copyright = {\copyright{} 2023 The Authors. Future Humanities published by John Wiley \& Sons Ltd.},
  langid = {english},
  keywords = {cascade_reading_group,computational philosophy,digital humanities,history of ideas,machine learning,natural language processing},
  annotation = {Read\_Status: To Read\\
Read\_Status\_Date: 2025-07-28T15:00:03.525Z},
  file = {C\:\\Users\\nasty\\Documents\\Zotero\\Brausch and Graßhoff - 2023 - Machine learning for the history of ideas.pdf;C\:\\Users\\nasty\\Zotero\\storage\\2FEQI3RM\\fhu2.html}
}

@misc{brigadirDetectingAttentionDominating2016,
  title = {Detecting {{Attention Dominating Moments Across Media Types}} - {{Tweet Stream}}},
  author = {Brigadir, Igor},
  year = 2016,
  pages = {106361822 Bytes},
  publisher = {figshare},
  doi = {10.6084/M9.FIGSHARE.2074105},
  urldate = {2025-07-28},
  abstract = {In this paper we address the problem of identifying attention dominating moments in online media. We are interested in discovering moments when everyone seems to be talking about the same thing. We investigate one particular aspect of breaking news: the tendency of multiple sources to concentrate attention on a single topic, leading to a collapse in diversity of content for a period of time. In this work we show that diversity at a topic level is effective for capturing this effect in blogs, in news articles, and on Twitter. The phenomenon is present in three distinctly different media types, each with their own unique features. We describe the phenomenon using case studies relating to major news stories from September 2015.},
  copyright = {Creative Commons Attribution 4.0 International},
  langid = {english},
  keywords = {FOS: Computer and information sciences,Information Systems,journal_article},
  annotation = {Read\_Status: To Read\\
Read\_Status\_Date: 2025-07-28T11:56:31.213Z},
  file = {C:\Users\nasty\Documents\Zotero\Brigadir - 2016 - Detecting Attention Dominating Moments Across Media Types - Tweet Stream.pdf}
}

@article{brownDynamicsLanguageChange2025,
  title = {Dynamics of {{Language Change}}: {{A Mixed-Methods Analysis}} of {{Language}} in an {{Online Transgender Community}}},
  shorttitle = {Dynamics of {{Language Change}}},
  author = {Brown, Cedar and Zimman, Lal and Todd, Simon},
  year = 2025,
  month = jun,
  journal = {Proceedings of the International AAAI Conference on Web and Social Media},
  volume = {19},
  pages = {289--306},
  issn = {2334-0770},
  doi = {10.1609/icwsm.v19i1.35817},
  urldate = {2025-07-01},
  abstract = {Trans-affirming language can be critical for trans people in transphobic sociopolitical contexts. But what kinds of trans activism -- and whose -- has determined what language is considered trans-affirming? Collective negotiations of trans language norms have long occurred across online platforms, providing rich data to explore what language change looks like in a community where language is highly politicized. In this paper, we use a mixed-methods approach to explore patterns of language change in one popular early online transmasculine community on LiveJournal. Using bigram snapshot language models, we establish canonical patterns of community-level change in aggregate language usage. We dig into how these patterns relate to population turnover by analyzing distinct groups of users, extending existing quantitative analysis to disentangle group-level variation from community-level change and combining it with qualitative analysis that scrutinizes the interactional contexts of change. We find influence of linguistic, interactional, and identity norms that are negotiated and enforced by highly active users, thus driving language change. Our analysis demonstrates the role of power in community-internal language negotiation, with implications for trans language change more broadly.},
  copyright = {Copyright (c) 2025 Association for the Advancement of Artificial Intelligence},
  langid = {english},
  keywords = {icwsm2025},
  annotation = {Read\_Status: To Read\\
Read\_Status\_Date: 2025-07-03T08:52:01.481Z},
  file = {C:\Users\nasty\Documents\Zotero\Brown et al. - 2025 - Dynamics of Language Change A Mixed-Methods Analysis of Language in an Online Transgender Community.pdf}
}

@article{bullinariaExtractingSemanticRepresentations2007,
  title = {Extracting Semantic Representations from Word Co-Occurrence Statistics: {{A}} Computational Study},
  shorttitle = {Extracting Semantic Representations from Word Co-Occurrence Statistics},
  author = {Bullinaria, John A. and Levy, Joseph P.},
  year = 2007,
  month = aug,
  journal = {Behavior Research Methods},
  volume = {39},
  number = {3},
  pages = {510--526},
  issn = {1554-3528},
  doi = {10.3758/BF03193020},
  urldate = {2024-12-11},
  abstract = {The idea that at least some aspects of word meaning can be induced from patterns of word co-occurrence is becoming increasingly popular. However, there is less agreement about the precise computations involved, and the appropriate tests to distinguish between the various possibilities. It is important that the effect of the relevant design choices and parameter values are understood if psychological models using these methods are to be reliably evaluated and compared. In this article, we present a systematic exploration of the principal computational possibilities for formulating and validating representations of word meanings from word co-occurrence statistics. We find that, once we have identified the best procedures, a very simple approach is surprisingly successful and robust over a range of psychologically relevant evaluation measures.},
  langid = {english},
  keywords = {Context Word,Latent Semantic Analysis,Semantic Representation,Semantic Task,Target Word},
  annotation = {Read\_Status: To Read\\
Read\_Status\_Date: 2025-07-28T11:57:39.022Z},
  file = {C:\Users\nasty\Documents\Zotero\Bullinaria and Levy - 2007 - Extracting semantic representations from word co-occurrence statistics A computational study 1.pdf}
}

@inproceedings{burovovaComputationalAnalysisDehumanization2024,
  title = {Computational {{Analysis}} of {{Dehumanization}} of {{Ukrainians}} on {{Russian Social Media}}},
  booktitle = {Proceedings of the 8th {{Joint SIGHUM Workshop}} on {{Computational Linguistics}} for {{Cultural Heritage}}, {{Social Sciences}}, {{Humanities}} and {{Literature}} ({{LaTeCH-CLfL}} 2024)},
  author = {Burovova, Kateryna and Romanyshyn, Mariana},
  editor = {Bizzoni, Yuri and {Degaetano-Ortlieb}, Stefania and Kazantseva, Anna and Szpakowicz, Stan},
  year = 2024,
  month = mar,
  pages = {28--39},
  publisher = {Association for Computational Linguistics},
  address = {St. Julians, Malta},
  urldate = {2024-12-12},
  abstract = {Dehumanization is a pernicious process of denying some or all attributes of humanness to the target group. It is frequently cited as a common hallmark of incitement to commit genocide. The international security landscape has seen a dramatic shift following the 2022 Russian invasion of Ukraine. This, coupled with recent developments in the conceptualization of dehumanization, necessitates the creation of new techniques for analyzing and detecting this extreme violence-related phenomenon on a large scale. Our project pioneers the development of a detection system for instances of dehumanization. To achieve this, we collected the entire posting history of the most popular bloggers on Russian Telegram and tested classical machine learning, deep learning, and zero-shot learning approaches to explore and detect the dehumanizing rhetoric. We found that the transformer-based method for entity extraction SpERT shows a promising result of F 1 = 0.85 for binary classification. The proposed methods can be built into the systems of anticipatory governance, contribute to the collection of evidence of genocidal intent in the Russian invasion of Ukraine, and pave the way for large-scale studies of dehumanizing language. This paper contains references to language that some readers may find offensive.},
  annotation = {Read\_Status: Read\\
Read\_Status\_Date: 2025-07-03T12:46:47.680Z},
  file = {C\:\\Users\\nasty\\Documents\\Zotero\\Burovova and Romanyshyn - 2024 - Computational Analysis of Dehumanization of Ukrainians on Russian Social Media 1.pdf;C\:\\Users\\nasty\\Documents\\Zotero\\Burovova and Romanyshyn - 2024 - Computational Analysis of Dehumanization of Ukrainians on Russian Social Media 1.pdf;C\:\\Users\\nasty\\Documents\\Zotero\\Burovova and Romanyshyn - 2024 - Computational Analysis of Dehumanization of Ukrainians on Russian Social Media 1.pdf}
}

@inproceedings{cassottiComputationalModelingSemantic2024,
  title = {Computational Modeling of Semantic Change},
  booktitle = {Proceedings of the 18th {{Conference}} of the {{European Chapter}} of the {{Association}} for {{Computational Linguistics}}: {{Tutorial Abstracts}}},
  author = {Cassotti, Pierluigi and Periti, Francesco and De Pascale, Stefano and Dubossarsky, Haim and Tahmasebi, Nina},
  editor = {Mesgar, Mohsen and Lo{\'a}iciga, Sharid},
  year = 2024,
  month = mar,
  pages = {1--8},
  publisher = {Association for Computational Linguistics},
  address = {St. Julian's, Malta},
  urldate = {2024-10-22},
  abstract = {Languages change constantly over time, influenced by social, technological, cultural and political factors that affect how people express themselves. In particular, words can undergo the process of semantic change, which can be subtle and significantly impact the interpretation of texts. For example, the word terrific used to mean `causing terror' and was as such synonymous to terrifying. Nowadays, speakers use the word in the sense of `excessive' and even `amazing'. In Historical Linguistics, tools and methods have been developed to analyse this phenomenon, including systematic categorisations of the types of change, the causes and the mechanisms underlying the different types of change. However, traditional linguistic methods, while informative, are often based on small, carefully curated samples. Thanks to the availability of both large diachronic corpora, the computational means to model word meaning unsupervised, and evaluation benchmarks, we are seeing an increasing interest in the computational modelling of semantic change. This is evidenced by the increasing number of publications in this new domain as well as the organisation of initiatives and events related to this topic, such as four editions of the International Workshop on Computational Approaches to Historical Language Change LChange1, and several evaluation campaigns (Schlechtweg et al., 2020a; Basile et al., 2020b; Kutuzov et al.; Zamora-Reina et al., 2022).},
  annotation = {Read\_Status: Read\\
Read\_Status\_Date: 2025-07-28T12:01:07.341Z},
  file = {C\:\\Users\\nasty\\Documents\\Zotero\\Cassotti et al. - 2024 - Computational modeling of semantic change 1.pdf;C\:\\Users\\nasty\\Documents\\Zotero\\Cassotti et al. - 2024 - Computational modeling of semantic change 2.pdf;C\:\\Users\\User\\OneDrive - Universität des Saarlandes\\Documents\\Zotero\\Cassotti et al. - 2024 - Computational modeling of semantic change.pdf}
}

@inproceedings{cassottiUsingSynchronicDefinitions2024,
  title = {Using {{Synchronic Definitions}} and {{Semantic Relations}} to {{Classify Semantic Change Types}}},
  booktitle = {Proceedings of the 62nd {{Annual Meeting}} of the {{Association}} for {{Computational Linguistics}} ({{Volume}} 1: {{Long Papers}})},
  author = {Cassotti, Pierluigi and De Pascale, Stefano and Tahmasebi, Nina},
  editor = {Ku, Lun-Wei and Martins, Andre and Srikumar, Vivek},
  year = 2024,
  month = aug,
  pages = {4539--4553},
  publisher = {Association for Computational Linguistics},
  address = {Bangkok, Thailand},
  doi = {10.18653/v1/2024.acl-long.249},
  urldate = {2025-07-28},
  abstract = {There is abundant evidence of the fact that the way words change their meaning can be classified in different types of change, highlighting the relationship between the old and new meanings (among which generalisation, specialisation and co-hyponymy transfer).In this paper, we present a way of detecting these types of change by constructing a model that leverages information both from synchronic lexical relations and definitions of word meanings. Specifically, we use synset definitions and hierarchy information from WordNet and test it on a digitized version of Blank's (1997) dataset of semantic change types. Finally, we show how the sense relationships can improve models for both approximation of human judgments of semantic relatedness as well as binary Lexical Semantic Change Detection.},
  annotation = {Read\_Status: To Read\\
Read\_Status\_Date: 2025-07-28T12:00:03.836Z},
  file = {C:\Users\nasty\Documents\Zotero\Cassotti et al. - 2024 - Using Synchronic Definitions and Semantic Relations to Classify Semantic Change Types.pdf}
}

@inproceedings{cassottiXLLEXEMEWiCPretrained2023,
  title = {{{XL-LEXEME}}: {{WiC Pretrained Model}} for {{Cross-Lingual LEXical sEMantic changE}}},
  shorttitle = {{{XL-LEXEME}}},
  booktitle = {Proceedings of the 61st {{Annual Meeting}} of the {{Association}} for {{Computational Linguistics}} ({{Volume}} 2: {{Short Papers}})},
  author = {Cassotti, Pierluigi and Siciliani, Lucia and DeGemmis, Marco and Semeraro, Giovanni and Basile, Pierpaolo},
  editor = {Rogers, Anna and {Boyd-Graber}, Jordan and Okazaki, Naoaki},
  year = 2023,
  month = jul,
  pages = {1577--1585},
  publisher = {Association for Computational Linguistics},
  address = {Toronto, Canada},
  doi = {10.18653/v1/2023.acl-short.135},
  urldate = {2025-07-28},
  abstract = {The recent introduction of large-scale datasets for the WiC (Word in Context) task enables the creation of more reliable and meaningful contextualized word embeddings.However, most of the approaches to the WiC task use cross-encoders, which prevent the possibility of deriving comparable word embeddings.In this work, we introduce XL-LEXEME, a Lexical Semantic Change Detection model.XL-LEXEME extends SBERT, highlighting the target word in the sentence. We evaluate XL-LEXEME on the multilingual benchmarks for SemEval-2020 Task 1 - Lexical Semantic Change (LSC) Detection and the RuShiftEval shared task involving five languages: English, German, Swedish, Latin, and Russian.XL-LEXEME outperforms the state-of-the-art in English, German and Swedish with statistically significant differences from the baseline results and obtains state-of-the-art performance in the RuShiftEval shared task.},
  annotation = {Read\_Status: To Read\\
Read\_Status\_Date: 2025-07-28T12:03:29.905Z},
  file = {C:\Users\nasty\Documents\Zotero\Cassotti et al. - 2023 - XL-LEXEME WiC Pretrained Model for Cross-Lingual LEXical sEMantic changE.pdf}
}

@inproceedings{chenChiWUGGraphbasedEvaluation2023,
  title = {{{ChiWUG}}: {{A Graph-based Evaluation Dataset}} for {{Chinese Lexical Semantic Change Detection}}},
  shorttitle = {{{ChiWUG}}},
  booktitle = {Proceedings of the 4th {{Workshop}} on {{Computational Approaches}} to {{Historical Language Change}}},
  author = {Chen, Jing and Chersoni, Emmanuele and Schlechtweg, Dominik and Prokic, Jelena and Huang, Chu-Ren},
  editor = {Tahmasebi, Nina and Montariol, Syrielle and Dubossarsky, Haim and Kutuzov, Andrey and Hengchen, Simon and Alfter, David and Periti, Francesco and Cassotti, Pierluigi},
  year = 2023,
  month = dec,
  pages = {93--99},
  publisher = {Association for Computational Linguistics},
  address = {Singapore},
  doi = {10.18653/v1/2023.lchange-1.10},
  urldate = {2024-12-17},
  abstract = {Recent studies suggested that language models are efficient tools for measuring lexical semantic change. In our paper, we present the compilation of the first graph-based evaluation dataset for lexical semantic change in the context of the Chinese language, specifically covering the periods of pre- and post- Reform and Opening Up. Exploiting the existing framework DURel, we collect over 61,000 human semantic relatedness judgments for 40 targets. The inferred word usage graphs and semantic change scores provide a basis for visualization and evaluation of semantic change.},
  annotation = {Read\_Status: To Read\\
Read\_Status\_Date: 2025-07-28T12:04:09.266Z},
  file = {C:\Users\nasty\Documents\Zotero\Chen et al. - 2023 - ChiWUG A Graph-based Evaluation Dataset for Chinese Lexical Semantic Change Detection 1.pdf}
}

@inproceedings{choHermitKingdomLens2025,
  title = {Hermit {{Kingdom Through}} the {{Lens}} of {{Multiple Perspectives}}: {{A Case Study}} of {{LLM Hallucination}} on {{North Korea}}},
  shorttitle = {Hermit {{Kingdom Through}} the {{Lens}} of {{Multiple Perspectives}}},
  booktitle = {Proceedings of the 31st {{International Conference}} on {{Computational Linguistics}}},
  author = {Cho, Eunjung and Cho, Won Ik and Seo, Soomin},
  editor = {Rambow, Owen and Wanner, Leo and Apidianaki, Marianna and {Al-Khalifa}, Hend and Eugenio, Barbara Di and Schockaert, Steven},
  year = 2025,
  month = jan,
  pages = {3353--3371},
  publisher = {Association for Computational Linguistics},
  address = {Abu Dhabi, UAE},
  urldate = {2025-02-05},
  abstract = {Hallucination in large language models (LLMs) remains a significant challenge for their safe deployment, particularly due to its potential to spread misinformation. Most existing solutions address this challenge by focusing on aligning the models with credible sources or by improving how models communicate their confidence (or lack thereof) in their outputs. While these measures may be effective in most contexts, they may fall short in scenarios requiring more nuanced approaches, especially in situations where access to accurate data is limited or determining credible sources is challenging. In this study, we take North Korea - a country characterised by an extreme lack of reliable sources and the prevalence of sensationalist falsehoods - as a case study. We explore and evaluate how some of the best-performing multilingual LLMs and specific language-based models generate information about North Korea in three languages spoken in countries with significant geo-political interests: English (United States, United Kingdom), Korean (South Korea), and Mandarin Chinese (China). Our findings reveal significant differences, suggesting that the choice of model and language can lead to vastly different understandings of North Korea, which has important implications given the global security challenges the country poses.},
  annotation = {Read\_Status: Not Reading\\
Read\_Status\_Date: 2025-10-29T12:44:18.639Z},
  file = {C:\Users\nasty\Documents\Zotero\Cho et al. - 2025 - Hermit Kingdom Through the Lens of Multiple Perspectives A Case Study of LLM Hallucination on North.pdf}
}

@article{coanComputerassistedClassificationContrarian2021,
  title = {Computer-Assisted Classification of Contrarian Claims about Climate Change},
  author = {Coan, Travis G. and Boussalis, Constantine and Cook, John and Nanko, Mirjam O.},
  year = 2021,
  month = nov,
  journal = {Scientific Reports},
  volume = {11},
  number = {1},
  pages = {22320},
  publisher = {Nature Publishing Group},
  issn = {2045-2322},
  doi = {10.1038/s41598-021-01714-4},
  urldate = {2024-09-30},
  abstract = {A growing body of scholarship investigates the role of misinformation in shaping the debate on climate change. Our research builds on and extends this literature by (1) developing and validating a comprehensive taxonomy of climate contrarianism, (2) conducting the largest content analysis to date on contrarian claims, (3) developing a computational model to accurately classify specific claims, and (4) drawing on an extensive corpus from conservative think-tank (CTTs) websites and contrarian blogs to construct a detailed history of claims over the past 20~years. Our study finds that the claims utilized by CTTs and contrarian blogs have focused on attacking the integrity of climate science and scientists and, increasingly, has challenged climate policy and renewable energy. We further demonstrate the utility of our approach by exploring the influence of corporate and foundation funding on the production and dissemination of specific contrarian claims.},
  copyright = {2021 The Author(s)},
  langid = {english},
  keywords = {Climate change,Environmental social sciences,Psychology and behaviour},
  annotation = {Read\_Status: Not Reading\\
Read\_Status\_Date: 2025-07-28T12:04:40.436Z},
  file = {C\:\\Users\\nasty\\Documents\\Zotero\\Coan et al. - 2021 - Computer-assisted classification of contrarian claims about climate change 2.pdf;C\:\\Users\\User\\OneDrive - Universität des Saarlandes\\Documents\\Zotero\\Coan et al. - 2021 - Computer-assisted classification of contrarian claims about climate change.pdf}
}

@article{cohenRussianWikipediasTop2023,
  title = {Russian {{Wikipedia}}'s {{Top Editor Leaves}} to {{Launch}} a {{Putin-Friendly Clone}}},
  author = {Cohen, Noam},
  year = 2023,
  month = jul,
  journal = {Bloomberg.com},
  urldate = {2025-09-04},
  abstract = {The move is seen as a potential precursor to a ban on the original version.},
  langid = {english},
  keywords = {ALPHABET INC-CL A,businessweek,Government,Jacob Rogers,Media,Moscow,politics,Russia,technology,Ukraine,United Nations Human Rights Council,Vladimir Putin,YANDEX NV-A},
  annotation = {Read\_Status: Not Reading\\
Read\_Status\_Date: 2025-09-04T18:29:26.691Z},
  file = {C:\Users\nasty\Zotero\storage\YHZPV6DI\russian-wikipedia-editor-leaves-to-launch-a-putin-friendly-clone.html}
}

@inproceedings{cooperHidingPlainSight2023,
  title = {Hiding in {{Plain Sight}}: {{Tweets}} with {{Hate Speech Masked}} by {{Homoglyphs}}},
  shorttitle = {Hiding in {{Plain Sight}}},
  booktitle = {Findings of the {{Association}} for {{Computational Linguistics}}: {{EMNLP}} 2023},
  author = {Cooper, Portia and Surdeanu, Mihai and Blanco, Eduardo},
  editor = {Bouamor, Houda and Pino, Juan and Bali, Kalika},
  year = 2023,
  month = dec,
  pages = {2922--2929},
  publisher = {Association for Computational Linguistics},
  address = {Singapore},
  doi = {10.18653/v1/2023.findings-emnlp.192},
  urldate = {2025-08-21},
  abstract = {To avoid detection by current NLP monitoring applications, progenitors of hate speech often replace one or more letters in offensive words with homoglyphs, visually similar Unicode characters. Harvesting real-world hate speech containing homoglyphs is challenging due to the vast replacement possibilities. We developed a character substitution scraping method and assembled the Offensive Tweets with Homoglyphs (OTH) Dataset (N=90,788) with more than 1.5 million occurrences of 1,281 non-Latin characters (emojis excluded). In an annotated sample (n=700), 40.14\% of the tweets were found to contain hate speech. We assessed the performance of seven transformer-based hate speech detection models and found that they performed poorly in a zero-shot setting (F1 scores between 0.04 and 0.52) but normalizing the data dramatically improved detection (F1 scores between 0.59 and 0.71). Training the models using the annotated data further boosted performance (highest micro-averaged F1 score=0.88, using five-fold cross validation). This study indicates that a dataset containing homoglyphs known and unknown to the scraping script can be collected, and that neural models can be trained to recognize camouflaged real-world hate speech.},
  annotation = {Read\_Status: To Read\\
Read\_Status\_Date: 2025-08-21T12:18:17.708Z},
  file = {C:\Users\nasty\Documents\Zotero\Cooper et al. - 2023 - Hiding in Plain Sight Tweets with Hate Speech Masked by Homoglyphs.pdf}
}

@book{cunninghamIdeaPropagandaReconstruction2002,
  title = {The Idea of Propaganda : A Reconstruction},
  shorttitle = {The Idea of Propaganda},
  author = {Cunningham, Stanley B.},
  year = 2002,
  publisher = {Westport, Conn. : Praeger},
  urldate = {2025-02-03},
  abstract = {x, 231 p. ; 25 cm; Includes bibliographical references (p. [209]-224) and index},
  collaborator = {{Internet Archive}},
  isbn = {978-0-275-97445-9},
  langid = {english},
  keywords = {propaganda_theory},
  annotation = {Read\_Status: To Read\\
Read\_Status\_Date: 2025-07-03T12:47:37.814Z},
  file = {C:\Users\nasty\Documents\Zotero\Cunningham - 2002 - The idea of propaganda  a reconstruction.pdf}
}

@inproceedings{dasanmartinoFineGrainedAnalysisPropaganda2019,
  title = {Fine-{{Grained Analysis}} of {{Propaganda}} in {{News Article}}},
  booktitle = {Proceedings of the 2019 {{Conference}} on {{Empirical Methods}} in {{Natural Language Processing}} and the 9th {{International Joint Conference}} on {{Natural Language Processing}} ({{EMNLP-IJCNLP}})},
  author = {Da San Martino, Giovanni and Yu, Seunghak and {Barr{\'o}n-Cede{\~n}o}, Alberto and Petrov, Rostislav and Nakov, Preslav},
  editor = {Inui, Kentaro and Jiang, Jing and Ng, Vincent and Wan, Xiaojun},
  year = 2019,
  month = nov,
  pages = {5636--5646},
  publisher = {Association for Computational Linguistics},
  address = {Hong Kong, China},
  doi = {10.18653/v1/D19-1565},
  urldate = {2025-02-03},
  abstract = {Propaganda aims at influencing people`s mindset with the purpose of advancing a specific agenda. Previous work has addressed propaganda detection at document level, typically labelling all articles from a propagandistic news outlet as propaganda. Such noisy gold labels inevitably affect the quality of any learning system trained on them. A further issue with most existing systems is the lack of explainability. To overcome these limitations, we propose a novel task: performing fine-grained analysis of texts by detecting all fragments that contain propaganda techniques as well as their type. In particular, we create a corpus of news articles manually annotated at fragment level with eighteen propaganda techniques and propose a suitable evaluation measure. We further design a novel multi-granularity neural network, and we show that it outperforms several strong BERT-based baselines.},
  keywords = {propaganda_theory},
  annotation = {Read\_Status: Read\\
Read\_Status\_Date: 2025-07-03T12:47:11.484Z},
  file = {C:\Users\nasty\Documents\Zotero\Da San Martino et al. - 2019 - Fine-Grained Analysis of Propaganda in News Article.pdf}
}

@inproceedings{dasanmartinoSemEval2020Task112020,
  title = {{{SemEval-2020 Task}} 11: {{Detection}} of {{Propaganda Techniques}} in {{News Articles}}},
  shorttitle = {{{SemEval-2020 Task}} 11},
  booktitle = {Proceedings of the {{Fourteenth Workshop}} on {{Semantic Evaluation}}},
  author = {Da San Martino, Giovanni and {Barr{\'o}n-Cede{\~n}o}, Alberto and Wachsmuth, Henning and Petrov, Rostislav and Nakov, Preslav},
  editor = {Herbelot, Aurelie and Zhu, Xiaodan and Palmer, Alexis and Schneider, Nathan and May, Jonathan and Shutova, Ekaterina},
  year = 2020,
  month = dec,
  pages = {1377--1414},
  publisher = {International Committee for Computational Linguistics},
  address = {Barcelona (online)},
  doi = {10.18653/v1/2020.semeval-1.186},
  urldate = {2025-07-28},
  abstract = {We present the results and the main findings of SemEval-2020 Task 11 on Detection of Propaganda Techniques in News Articles. The task featured two subtasks. Subtask SI is about Span Identification: given a plain-text document, spot the specific text fragments containing propaganda. Subtask TC is about Technique Classification: given a specific text fragment, in the context of a full document, determine the propaganda technique it uses, choosing from an inventory of 14 possible propaganda techniques. The task attracted a large number of participants: 250 teams signed up to participate and 44 made a submission on the test set. In this paper, we present the task, analyze the results, and discuss the system submissions and the methods they used. For both subtasks, the best systems used pre-trained Transformers and ensembles.},
  annotation = {Read\_Status: Read\\
Read\_Status\_Date: 2025-08-29T12:37:11.055Z},
  file = {C:\Users\nasty\Documents\Zotero\Da San Martino et al. - 2020 - SemEval-2020 Task 11 Detection of Propaganda Techniques in News Articles.pdf}
}

@inproceedings{dasanmartinoSurveyComputationalPropaganda2021,
  title = {A Survey on Computational Propaganda Detection},
  booktitle = {Proceedings of the {{Twenty-Ninth International Joint Conference}} on {{Artificial Intelligence}}},
  author = {Da San Martino, Giovanni and Cresci, Stefano and {Barr{\'o}n-Cede{\~n}o}, Alberto and Yu, Seunghak and Di Pietro, Roberto and Nakov, Preslav},
  year = 2021,
  month = jan,
  series = {{{IJCAI}}'20},
  pages = {4826--4832},
  address = {Yokohama, Japan},
  urldate = {2024-12-10},
  abstract = {Propaganda campaigns aim at influencing people's mindset with the purpose of advancing a specific agenda. They exploit the anonymity of the Internet, the micro-profiling ability of social networks, and the ease of automatically creating and managing coordinated networks of accounts, to reach millions of social network users with persuasive messages, specifically targeted to topics each individual user is sensitive to, and ultimately influencing the outcome on a targeted issue. In this survey, we review the state of the art on computational propaganda detection from the perspective of Natural Language Processing and Network Analysis, arguing about the need for combined efforts between these communities. We further discuss current challenges and future research directions.},
  isbn = {978-0-9992411-6-5},
  annotation = {Read\_Status: To Read\\
Read\_Status\_Date: 2025-07-03T12:48:28.071Z},
  file = {C:\Users\nasty\Documents\Zotero\Da San Martino et al. - 2021 - A survey on computational propaganda detection 1.pdf}
}

@inproceedings{degaetano-ortliebModelingIntratextualVariation2017,
  title = {Modeling Intra-Textual Variation with Entropy and Surprisal: Topical vs. Stylistic Patterns},
  shorttitle = {Modeling Intra-Textual Variation with Entropy and Surprisal},
  booktitle = {Proceedings of the {{Joint SIGHUM Workshop}} on {{Computational Linguistics}} for {{Cultural Heritage}}, {{Social Sciences}}, {{Humanities}} and {{Literature}}},
  author = {{Degaetano-Ortlieb}, Stefania and Teich, Elke},
  editor = {Alex, Beatrice and {Degaetano-Ortlieb}, Stefania and Feldman, Anna and Kazantseva, Anna and Reiter, Nils and Szpakowicz, Stan},
  year = 2017,
  month = aug,
  pages = {68--77},
  publisher = {Association for Computational Linguistics},
  address = {Vancouver, Canada},
  doi = {10.18653/v1/W17-2209},
  urldate = {2025-05-16},
  abstract = {We present a data-driven approach to investigate intra-textual variation by combining entropy and surprisal. With this approach we detect linguistic variation based on phrasal lexico-grammatical patterns across sections of research articles. Entropy is used to detect patterns typical of specific sections. Surprisal is used to differentiate between more and less informationally-loaded patterns as well as type of information (topical vs. stylistic). While we here focus on research articles in biology/genetics, the methodology is especially interesting for digital humanities scholars, as it can be applied to any text type or domain and combined with additional variables (e.g. time, author or social group).},
  annotation = {Read\_Status: To Read\\
Read\_Status\_Date: 2025-07-28T12:15:16.350Z},
  file = {C:\Users\nasty\Documents\Zotero\Degaetano-Ortlieb and Teich - 2017 - Modeling intra-textual variation with entropy and surprisal topical vs. stylistic patterns.pdf}
}

@article{degaetano-ortliebOptimalCodeCommunication2022,
  title = {{Toward an Optimal Code for Communication: {{The}} Case of Scientific {{English}}}},
  shorttitle = {Toward an Optimal Code for Communication},
  author = {{Degaetano-Ortlieb}, Stefania and Teich, Elke},
  year = 2019,
  month = feb,
  journal = {Corpus Linguistics and Linguistic Theory},
  volume = {18},
  number = {1},
  pages = {175--207},
  publisher = {De Gruyter Mouton},
  issn = {1613-7035},
  doi = {10.1515/cllt-2018-0088},
  urldate = {2024-10-01},
  abstract = {We present a model of the linguistic development of scientific English from the mid-seventeenth to the late-nineteenth century, a period that witnessed significant political and social changes, including the evolution of modern science. There is a wealth of descriptive accounts of scientific English, both from a synchronic and a diachronic perspective, but only few attempts at a unified explanation of its evolution. The explanation we offer here is a communicative one: while external pressures (specialization, diversification) push for an increase in expressivity, communicative concerns pull toward convergence on particular options (conventionalization). What emerges over time is a code which is optimized for written, specialist communication, relying on specific linguistic means to modulate information content. As we show, this is achieved by the systematic interplay between lexis and grammar. The corpora we employ are the Royal Society Corpus (RSC) and for comparative purposes, the Corpus of Late Modern English (CLMET). We build various diachronic, computational n-gram language models of these corpora and then apply formal measures of information content (here: relative entropy and surprisal) to detect the linguistic features significantly contributing to diachronic change, estimate the (changing) level of information of features and capture the time course of change.},
  copyright = {De Gruyter expressly reserves the right to use all content for commercial text and data mining within the meaning of Section 44b of the German Copyright Act.},
  langid = {english},
  keywords = {diachronic change,Kullback-Leibler Divergence,scientific English,surprisal},
  annotation = {Read\_Status: Read\\
Read\_Status\_Date: 2025-07-03T08:49:40.659Z},
  file = {C\:\\Users\\nasty\\Documents\\Zotero\\Degaetano-Ortlieb and Teich - 2022 - Toward an optimal code for communication The case of scientific English 1.pdf;C\:\\Users\\nasty\\Documents\\Zotero\\Degaetano-Ortlieb and Teich - 2022 - Toward an optimal code for communication The case of scientific English 1.pdf;C\:\\Users\\nasty\\Documents\\Zotero\\Degaetano-Ortlieb and Teich - 2022 - Toward an optimal code for communication The case of scientific English 2.pdf;C\:\\Users\\nasty\\Documents\\Zotero\\Degaetano-Ortlieb and Teich - 2022 - Toward an optimal code for communication The case of scientific English.pdf}
}

@article{degaetano-ortliebRegisterialAdaptationVs2021,
  title = {Registerial {{Adaptation}} vs. {{Innovation Across Situational Contexts}}: 18th {{Century Women}} in {{Transition}}},
  shorttitle = {Registerial {{Adaptation}} vs. {{Innovation Across Situational Contexts}}},
  author = {{Degaetano-Ortlieb}, Stefania and S{\"a}ily, Tanja and Bizzoni, Yuri},
  year = 2021,
  month = jun,
  journal = {Frontiers in Artificial Intelligence},
  volume = {4},
  publisher = {Frontiers},
  issn = {2624-8212},
  doi = {10.3389/frai.2021.609970},
  urldate = {2024-12-10},
  abstract = {{$<$}p{$>$}Endeavors to computationally model language variation and change are ever increasing. While analyses of recent diachronic trends are frequently conducted, long-term trends accounting for sociolinguistic variation are less well-studied. Our work sheds light on the temporal dynamics of language use of British 18th century women as a group in transition across two situational contexts. Our findings reveal that in formal contexts women adapt to register conventions, while in informal contexts they act as innovators of change in language use influencing others. While adopted from other disciplines, our methods inform (historical) sociolinguistic work in novel ways. These methods include diachronic periodization by Kullback-Leibler divergence to determine periods of change and relevant features of variation, and event cascades as influencer models.{$<$}/p{$>$}},
  langid = {english},
  keywords = {computational sociolinguistics,Computational sociolinguistics,diachronic variation in language use,gender-specific linguistic variation,Gender-specific linguistic variation,historical sociolinguistics,Historical sociolinguistics,Late Modern English,linguistic innovation,Linguistic innovation,periods of change in language use,register variation,Register variation},
  annotation = {Read\_Status: To Read\\
Read\_Status\_Date: 2025-07-28T12:14:18.940Z},
  file = {C\:\\Users\\nasty\\Documents\\Zotero\\Degaetano-Ortlieb et al. - 2021 - Registerial Adaptation vs. Innovation Across Situational Contexts 18th Century Women in Transition 2.pdf;C\:\\Users\\User\\OneDrive - Universität des Saarlandes\\Documents\\Zotero\\Degaetano-Ortlieb et al. - 2021 - Registerial Adaptation vs. Innovation Across Situational Contexts 18th Century Women in Transition.pdf;C\:\\Users\\User\\OneDrive - Universität des Saarlandes\\Documents\\Zotero\\Säily - Registerial Adaptation vs. Innovation Across Situational Contexts 18th Century Women in Transition.pdf}
}

@article{diengTopicModelingEmbedding2020,
  title = {Topic {{Modeling}} in {{Embedding Spaces}}},
  author = {Dieng, Adji B. and Ruiz, Francisco J. R. and Blei, David M.},
  editor = {Johnson, Mark and Roark, Brian and Nenkova, Ani},
  year = 2020,
  journal = {Transactions of the Association for Computational Linguistics},
  volume = {8},
  pages = {439--453},
  publisher = {MIT Press},
  address = {Cambridge, MA},
  doi = {10.1162/tacl_a_00325},
  urldate = {2025-01-30},
  abstract = {Topic modeling analyzes documents to learn meaningful patterns of words. However, existing topic models fail to learn interpretable topics when working with large and heavy-tailed vocabularies. To this end, we develop the embedded topic model (etm), a generative model of documents that marries traditional topic models with word embeddings. More specifically, the etm models each word with a categorical distribution whose natural parameter is the inner product between the word`s embedding and an embedding of its assigned topic. To fit the etm, we develop an efficient amortized variational inference algorithm. The etm discovers interpretable topics even with large vocabularies that include rare words and stop words. It outperforms existing document models, such as latent Dirichlet allocation, in terms of both topic quality and predictive performance.},
  annotation = {Read\_Status: Not Reading\\
Read\_Status\_Date: 2025-07-28T12:17:38.048Z},
  file = {C:\Users\nasty\Documents\Zotero\Dieng et al. - 2020 - Topic Modeling in Embedding Spaces.pdf}
}

@inproceedings{dubossarskyTimeOutTemporalReferencing2019,
  title = {Time-{{Out}}: {{Temporal Referencing}} for {{Robust Modeling}} of {{Lexical Semantic Change}}},
  shorttitle = {Time-{{Out}}},
  booktitle = {Proceedings of the 57th {{Annual Meeting}} of the {{Association}} for {{Computational Linguistics}}},
  author = {Dubossarsky, Haim and Hengchen, Simon and Tahmasebi, Nina and Schlechtweg, Dominik},
  editor = {Korhonen, Anna and Traum, David and M{\`a}rquez, Llu{\'i}s},
  year = 2019,
  month = jul,
  pages = {457--470},
  publisher = {Association for Computational Linguistics},
  address = {Florence, Italy},
  doi = {10.18653/v1/P19-1044},
  urldate = {2024-11-07},
  abstract = {State-of-the-art models of lexical semantic change detection suffer from noise stemming from vector space alignment. We have empirically tested the Temporal Referencing method for lexical semantic change and show that, by avoiding alignment, it is less affected by this noise. We show that, trained on a diachronic corpus, the skip-gram with negative sampling architecture with temporal referencing outperforms alignment models on a synthetic task as well as a manual testset. We introduce a principled way to simulate lexical semantic change and systematically control for possible biases.},
  annotation = {Read\_Status: To Read\\
Read\_Status\_Date: 2025-07-28T12:18:19.921Z},
  file = {C\:\\Users\\nasty\\Documents\\Zotero\\Dubossarsky et al. - 2019 - Time-Out Temporal Referencing for Robust Modeling of Lexical Semantic Change 2.pdf;C\:\\Users\\User\\OneDrive - Universität des Saarlandes\\Documents\\Zotero\\Dubossarsky et al. - 2019 - Time-Out Temporal Referencing for Robust Modeling of Lexical Semantic Change.pdf}
}

@article{duttaHawkesEyeDetectingFake2020,
  title = {{{HawkesEye}}: {{Detecting Fake Retweeters Using Hawkes Process}} and {{Topic Modeling}}},
  shorttitle = {{{HawkesEye}}},
  author = {Dutta, Hridoy Sankar and Dutta, Vishal Raj and Adhikary, Aditya and Chakraborty, Tanmoy},
  year = 2020,
  journal = {IEEE Transactions on Information Forensics and Security},
  volume = {15},
  pages = {2667--2678},
  issn = {1556-6021},
  doi = {10.1109/TIFS.2020.2970601},
  urldate = {2025-07-24},
  abstract = {Retweets are essential to boost the popularity of a tweet, and a large number of fake retweeters can contribute heavily to this aspect. We define a fake retweeter as a Twitter account that retweets spammy tweets, retweets an abnormally large amount of tweets in a short period, or misuses a trending hashtag to promote events irrelevant to the topic of discussion. We introduce an up-to-date, temporally diverse, trend-oriented labeled dataset to address the problem of fake retweeter detection. We develop a novel classifier, called HawkesEye which makes predictions based on a temporal window, in contrast to existing approaches which require a graph-like relationship between tweet entities, or the presence of the entire retweeting timeline of a retweeter. HawkesEye utilizes both temporal and textual information using a class-specific topic model and Hawkes processes. Experiments on our curated dataset show significant improvement over four state-of-the-art methods, with precision and recall scores of 0.964 and 0.960 on a balanced dataset, respectively -- HawkesEye beats the best baseline by 6.16\% and 25.98\% relative improvement in terms of precision and recall, respectively. We also diagnose our model to understand the advantages and pitfalls of the underlying mechanism. We believe that the extent of this study is not restricted to Twitter, but generalizable to other social media systems such as Facebook and Instagram with similar reposting capabilities.},
  keywords = {artificial boost,Data models,Ecosystems,Facebook,Fake retweeters,Hawkes processes,Market research,online media,social growth,Tagging,Twitter},
  annotation = {Read\_Status: To Read\\
Read\_Status\_Date: 2025-07-28T12:18:47.168Z},
  file = {C\:\\Users\\nasty\\Documents\\Zotero\\Dutta et al. - 2020 - HawkesEye Detecting Fake Retweeters Using Hawkes Process and Topic Modeling.pdf;C\:\\Users\\nasty\\Zotero\\storage\\WQWWE5U8\\8976235.html}
}

@incollection{egginsGenresRegistersDiscourse1997,
  title = {Genres and Registers of Discourse},
  booktitle = {Discourse as Structure and Process:  {{Discourse}} Studies:  {{A}} Multidisciplinary Introduction, {{Vol}}. 1.},
  author = {Eggins, Suzanne and Martin, J. R.},
  year = 1997,
  pages = {230--256},
  publisher = {Sage Publications, Inc},
  address = {Thousand Oaks, CA, US},
  doi = {10.4135/9781446221884.n9},
  abstract = {introduces register and genre theory (R{$>$}), a label which can be applied to a range of linguistic approaches to discourse which seek to theorise how discourses, or texts, are like and unlike each other, and why the terms register (context of situation) and genre (context of culture) identify the 2 major layers of context which have an impact on text, and are therefore the 2 main dimensions of variation between texts / within the approach outlined here, register and genre variation are 2 realizational planes in a social semiotic view of text / this view is inherently dialogic and interactive: text is both the realization of types of context, and the enactment of what matters to cultural members in situations / just as texts are not neutral encodings of a natural reality but semiotic constructions of socially constructed meanings, so the task of R{$>$} is not merely the description of linguistic variation between texts / it must also involve analysts in exposing and explaining how texts serve divergent interests in the discursive construction of social life---including the interests of the discourse analysts themselves (PsycINFO Database Record (c) 2019 APA, all rights reserved)},
  isbn = {978-0-8039-7844-7 978-0-8039-7845-4},
  keywords = {Discourse Analysis,Linguistics,narrative_theory,Semiotics,Social Interaction,Text Analysis,Text Structure,Theories},
  annotation = {Read\_Status: To Read\\
Read\_Status\_Date: 2025-08-20T13:15:57.401Z},
  file = {C:\Users\nasty\Documents\Zotero\Eggins and Martin - 1997 - Genres and registers of discourse.pdf}
}

@article{eisensteinDiffusionLexicalChange2014,
  title = {Diffusion of {{Lexical Change}} in {{Social Media}}},
  author = {Eisenstein, Jacob and O'Connor, Brendan and Smith, Noah A. and Xing, Eric P.},
  year = 2014,
  month = nov,
  journal = {PLOS ONE},
  volume = {9},
  number = {11},
  pages = {e113114},
  publisher = {Public Library of Science},
  issn = {1932-6203},
  doi = {10.1371/journal.pone.0113114},
  urldate = {2025-08-08},
  abstract = {Computer-mediated communication is driving fundamental changes in the nature of written language. We investigate these changes by statistical analysis of a dataset comprising 107 million Twitter messages (authored by 2.7 million unique user accounts). Using a latent vector autoregressive model to aggregate across thousands of words, we identify high-level patterns in diffusion of linguistic change over the United States. Our model is robust to unpredictable changes in Twitter's sampling rate, and provides a probabilistic characterization of the relationship of macro-scale linguistic influence to a set of demographic and geographic predictors. The results of this analysis offer support for prior arguments that focus on geographical proximity and population size. However, demographic similarity -- especially with regard to race -- plays an even more central role, as cities with similar racial demographics are far more likely to share linguistic influence. Rather than moving towards a single unified ``netspeak'' dialect, language evolution in computer-mediated communication reproduces existing fault lines in spoken American English.},
  langid = {english},
  keywords = {Cities,Dynamical systems,Evolutionary linguistics,Language,Linguistic geography,Social media,Sociolinguistics,Urban areas},
  annotation = {Read\_Status: To Read\\
Read\_Status\_Date: 2025-08-20T13:15:17.793Z},
  file = {C:\Users\nasty\Documents\Zotero\Eisenstein et al. - 2014 - Diffusion of Lexical Change in Social Media.pdf}
}

@article{engelSocialDynamicsMobilization2025,
  title = {Social {{Dynamics}} and {{Mobilization Potential}} of {{Online Election Narratives}}},
  author = {Engel, Kristen and Mitra, Tanushree and Spiro, Emma S.},
  year = 2025,
  month = jun,
  journal = {Proceedings of the International AAAI Conference on Web and Social Media},
  volume = {19},
  pages = {479--496},
  issn = {2334-0770},
  doi = {10.1609/icwsm.v19i1.35827},
  urldate = {2025-07-01},
  abstract = {The fragmentation of social media challenges how we might efficiently and effectively identify, understand, and counter harmful content. Prior work establishes frameworks for measuring problematic narratives, evaluating harms, and leveraging interdisciplinary theories and findings to design mitigating solutions. However, there is little understanding of these phenomena outside of mainstream platforms. This is particularly concerning given that alt-tech users have been observed to include insurrectionists, active shooters, and other extremists -- often driven from mainstream platforms due to deplatforming and content moderation . Our work aims to characterize online narratives across alt-tech platforms. In particular, we highlight how rumoring and conspiracy theory narratives in the context of the 2022 U.S. elections impact social dynamics and inspire collective action. We gather a unique dataset of over 7,000 social media posts from Gab, Gettr, Parler, and Truth Social from which we derive prevalent narratives using natural language processing techniques. We then examine how the platform, affect, and engagement differ across context through the lens of narrative, social identity, and mobilization potential using mixed methods. Findings from our analyses show variation between how narratives support social identity conceptions of power and mobilization potential.},
  copyright = {Copyright (c) 2025 Association for the Advancement of Artificial Intelligence},
  langid = {english},
  keywords = {icwsm2025},
  annotation = {Read\_Status: To Read\\
Read\_Status\_Date: 2025-07-03T11:35:04.903Z},
  file = {C:\Users\nasty\Documents\Zotero\Engel et al. - 2025 - Social Dynamics and Mobilization Potential of Online Election Narratives.pdf}
}

@article{entmanFramingBiasMedia2007,
  title = {Framing {{Bias}}: {{Media}} in the {{Distribution}} of {{Power}}},
  shorttitle = {Framing {{Bias}}},
  author = {Entman, Robert M.},
  year = 2007,
  month = mar,
  journal = {Journal of Communication},
  volume = {57},
  number = {1},
  pages = {163--173},
  issn = {00219916, 14602466},
  doi = {10.1111/j.1460-2466.2006.00336.x},
  urldate = {2025-07-28},
  copyright = {http://doi.wiley.com/10.1002/tdm\_license\_1.1},
  langid = {english},
  keywords = {Democracy,Framing Effects,Mass Media,Priming,Public Opinion},
  annotation = {Read\_Status: To Read\\
Read\_Status\_Date: 2025-07-28T15:51:45.539Z},
  file = {C\:\\Users\\nasty\\Documents\\Zotero\\Entman - 2007 - Framing Bias Media in the Distribution of Power.pdf;C\:\\Users\\User\\OneDrive - Universität des Saarlandes\\Documents\\Zotero\\Entman - 2007 - Framing Bias Media in the Distribution of Power.pdf;C\:\\Users\\nasty\\Zotero\\storage\\G64U65CY\\j.1460-2466.2006.00336.html;C\:\\Users\\nasty\\Zotero\\storage\\WNB7KUN6\\2007-04510-047.html}
}

@article{entmanFramingClarificationFractured1993,
  title = {Framing: {{Toward Clarification}} of a {{Fractured Paradigm}}},
  shorttitle = {Framing},
  author = {Entman, Robert M.},
  year = 1993,
  journal = {Journal of Communication},
  volume = {43},
  number = {4},
  pages = {51--58},
  issn = {1460-2466},
  doi = {10.1111/j.1460-2466.1993.tb01304.x},
  urldate = {2025-01-08},
  langid = {english},
  annotation = {Read\_Status: To Read\\
Read\_Status\_Date: 2025-07-28T12:28:29.883Z},
  file = {C\:\\Users\\nasty\\Documents\\Zotero\\Entman - 1993 - Framing Toward Clarification of a Fractured Paradigm.pdf;C\:\\Users\\nasty\\Zotero\\storage\\HWL7EQ79\\j.1460-2466.1993.tb01304.html}
}

@inproceedings{fankhauserExploringVisualizingVariation2014,
  title = {Exploring and {{Visualizing Variation}} in {{Language Resources}}},
  booktitle = {Proceedings of the {{Ninth International Conference}} on {{Language Resources}} and {{Evaluation}} ({{LREC}}'14)},
  author = {Fankhauser, Peter and Knappen, J{\"o}rg and Teich, Elke},
  editor = {Calzolari, Nicoletta and Choukri, Khalid and Declerck, Thierry and Loftsson, Hrafn and Maegaard, Bente and Mariani, Joseph and Moreno, Asuncion and Odijk, Jan and Piperidis, Stelios},
  year = 2014,
  month = may,
  pages = {4125--4128},
  publisher = {European Language Resources Association (ELRA)},
  address = {Reykjavik, Iceland},
  urldate = {2024-12-10},
  abstract = {Language resources are often compiled for the purpose of variational analysis, such as studying differences between genres, registers, and disciplines, regional and diachronic variation, influence of gender, cultural context, etc. Often the sheer number of potentially interesting contrastive pairs can get overwhelming due to the combinatorial explosion of possible combinations. In this paper, we present an approach that combines well understood techniques for visualization heatmaps and word clouds with intuitive paradigms for exploration drill down and side by side comparison to facilitate the analysis of language variation in such highly combinatorial situations. Heatmaps assist in analyzing the overall pattern of variation in a corpus, and word clouds allow for inspecting variation at the level of words.},
  annotation = {Read\_Status: To Read\\
Read\_Status\_Date: 2025-07-28T12:28:56.706Z},
  file = {C:\Users\nasty\Documents\Zotero\Fankhauser et al. - 2014 - Exploring and Visualizing Variation in Language Resources 1.pdf}
}

@article{fellowsPropagandaCommunicationStudy1957,
  title = {Propaganda and {{Communication}}: {{A Study}} in {{Definitions}}},
  shorttitle = {Propaganda and {{Communication}}},
  author = {Fellows, Erwin W.},
  year = 1957,
  month = dec,
  journal = {Journalism Quarterly},
  volume = {34},
  number = {4},
  pages = {431--442},
  publisher = {SAGE Publications},
  issn = {0022-5533},
  doi = {10.1177/107769905703400401},
  urldate = {2025-02-03},
  abstract = {The author discusses definitions of propaganda, then suggests a frame of reference for distinguishing it from other types of communication.},
  langid = {english},
  keywords = {propaganda_theory},
  annotation = {Read\_Status: Read\\
Read\_Status\_Date: 2025-09-04T18:28:14.354Z},
  file = {C:\Users\nasty\Documents\Zotero\Fellows - 1957 - Propaganda and Communication A Study in Definitions.pdf}
}

@inproceedings{fischerContemporaryNewsCorpus2024,
  title = {A {{Contemporary News Corpus}} of {{Ukrainian}} ({{CNC-UA}}): {{Compilation}}, {{Annotation}}, {{Publication}}},
  shorttitle = {A {{Contemporary News Corpus}} of {{Ukrainian}} ({{CNC-UA}})},
  booktitle = {Proceedings of the {{Third Ukrainian Natural Language Processing Workshop}} ({{UNLP}}) @ {{LREC-COLING}} 2024},
  author = {Fischer, Stefan and Haidarzhyi, Kateryna and Knappen, J{\"o}rg and Polishchuk, Olha and Stodolinska, Yuliya and Teich, Elke},
  editor = {Romanyshyn, Mariana and Romanyshyn, Nataliia and Hlybovets, Andrii and Ignatenko, Oleksii},
  year = 2024,
  month = may,
  pages = {1--7},
  publisher = {{ELRA and ICCL}},
  address = {Torino, Italia},
  urldate = {2024-10-16},
  abstract = {We present a corpus of contemporary Ukrainian news articles published between 2019 and 2022 on the news website of the national public broadcaster of Ukraine, commonly known as SUSPILNE. The current release comprises 87 210 364 words in 292 955 texts. Texts are annotated with titles and their time of publication. In addition, the corpus has been linguistically annotated at the token level with a dependency parser. To provide further aspects for investigation, a topic model was trained on the corpus. The corpus is hosted (Fischer et al., 2023) at the Saarbr\"ucken CLARIN center under a CC BY-NC-ND 4.0 license and available in two tab-separated formats: CoNLL-U (de Marneffe et al., 2021) and vertical text format (VRT) as used by the IMS Open Corpus Workbench (CWB; Evert and Hardie, 2011) and CQPweb (Hardie, 2012). We show examples of using the CQPweb interface, which allows to extract the quantitative data necessary for distributional and collocation analyses of the CNC-UA. As the CNC-UA contains news texts documenting recent events, it is highly relevant not only for linguistic analyses of the modern Ukrainian language but also for socio-cultural and political studies.},
  annotation = {Read\_Status: Not Reading\\
Read\_Status\_Date: 2025-07-28T12:29:18.169Z},
  file = {C\:\\Users\\nasty\\Documents\\Zotero\\Fischer et al. - 2024 - A Contemporary News Corpus of Ukrainian (CNC-UA) Compilation, Annotation, Publication 2.pdf;C\:\\Users\\User\\OneDrive - Universität des Saarlandes\\Documents\\Zotero\\Fischer et al. - 2024 - A Contemporary News Corpus of Ukrainian (CNC-UA) Compilation, Annotation, Publication.pdf}
}

@article{fitzmauriceVolatileConceptsAnalysing2022,
  title = {Volatile Concepts: {{Analysing}} Discursive Change through Underspecification in Co-Occurrence Quads},
  shorttitle = {Volatile Concepts},
  author = {Fitzmaurice, Susan and Mehl, Seth},
  year = 2022,
  month = oct,
  journal = {International Journal of Corpus Linguistics},
  volume = {27},
  number = {4},
  pages = {428--450},
  issn = {1384-6655, 1569-9811},
  doi = {10.1075/ijcl.22005.fit},
  urldate = {2025-09-22},
  abstract = {Abstract                            This paper demonstrates the value of studying co-occurrence `quads'~-- constellations of four non-adjacent lemmas that consistently co-occur across spans of up to 100 tokens~-- for understanding discursive change. We map meaning onto quads as `discursive concepts', which encompass encyclopaedic semantics, pragmatics, and context. We investigate a high-frequency quad with high co-occurrence strength in EEBO-TCP:               world-heaven-earth-power               . We conduct semantic and pragmatic analysis to generate hypotheses regarding discursive change. The quad's components are semantically underspecified; thus, although the quad indicates a discursive concept, each instantiation of the quad is variable, contingent, and dependent upon context and pragmatic processes for interpretation. We observe how the vague lexemes that constitute building blocks of religious discourse are employed to generate new, timely secular discourses; and we argue that semantic underspecification is the site and source of discursive change. Indeed, the volatile, unstable nature of the component lexical meanings renders them indispensable to early modern debate.},
  copyright = {http://creativecommons.org/licenses/by/4.0/},
  langid = {english},
  keywords = {cascade_reading_group},
  annotation = {Read\_Status: Read\\
Read\_Status\_Date: 2025-09-24T13:01:28.976Z},
  file = {C:\Users\nasty\Documents\Zotero\Fitzmaurice and Mehl - 2022 - Volatile concepts Analysing discursive change through underspecification in co-occurrence quads.pdf}
}

@article{fortuinUkraineCommitsGenocide2022,
  title = {``{{Ukraine}} Commits Genocide on {{Russians}}'': The Term ``Genocide'' in {{Russian}} Propaganda},
  shorttitle = {``{{Ukraine}} Commits Genocide on {{Russians}}''},
  author = {Fortuin, Egbert},
  year = 2022,
  month = nov,
  journal = {Russian Linguistics},
  volume = {46},
  number = {3},
  pages = {313--347},
  issn = {0304-3487, 1572-8714},
  doi = {10.1007/s11185-022-09258-5},
  urldate = {2024-12-13},
  abstract = {Abstract                            According to Russia, the main reason for starting the war with Ukraine on February 24, 2022 was the genocide of the Russian-speaking population by the Nazi government of Volodymyr Zelenskyy. In this paper I investigate the Russian claims about genocide. These claims are shown to be part of the rhetorical frame               Russophobic Nazi Ukraine government commits genocide on Russians               , an aspect of Russian propaganda which builds on the Second World War. I demonstrated that this frame is itself an aspect of a more abstract frame, where any perceived enemy of Russia, is portrayed as a fascist or Nazi force that aims to destroy Russia and Russian culture. I investigate how this frame emerged and developed over time in five stages, starting in post-Soviet Russia and Ukraine, and in Russia after the Orange and Maidan revolutions in Ukraine. Russia tries to provide argumentation for the validity of the frame by referring to a number of objectively established facts relating to the situation in the Donbas, Ukraine's language laws, and the presence of right-extremists in Ukraine. In doing so, some ideas are made more salient than others (the language laws that diminish the official status of Russian in Ukraine and the hostilities in the Donbas region), while other ideas are suppressed altogether (e.g. the juridical meaning of genocide, the actual status of right-wing extremism in Ukraine, the role of Russia in the hostilities in the Donbas, and the actual status of the language laws in Ukraine). In all of these cases, the arguments used by Russia are not valid, being based on exaggerations, hyperbolic use of terminology, and lies. The main goal of this frame is to acquire and retain support for Russia's policy toward Ukraine, as well as to deflect any potential criticism on Russia itself. Ultimately, the Russian propaganda is part of the concept               Russkij Mir               `Russian World', where Russian language and culture are a means to restore President Vladimir Putin's Russian sphere of influence from Soviet times or earlier.},
  langid = {english},
  annotation = {Read\_Status: Not Reading\\
Read\_Status\_Date: 2025-07-28T12:29:31.947Z},
  file = {C:\Users\nasty\Documents\Zotero\Fortuin - 2022 - “Ukraine commits genocide on Russians” the term “genocide” in Russian propaganda 1.pdf}
}

@misc{FramingUSCoverage,
  title = {Framing {{U}}.{{S}}. {{Coverage}} of {{International News}}: {{Contrast}} in {{Narratives}} of the {{KAL}} and {{Iran Air Incidents}}},
  shorttitle = {Framing {{U}}.{{S}}. {{Coverage}} of {{International News}}},
  journal = {ResearchGate},
  urldate = {2025-01-07},
  abstract = {Access 135+ million publications and connect with 20+ million researchers. Join for free and gain visibility by uploading your research.},
  howpublished = {https://www.researchgate.net/publication/229461133\_Framing\_US\_Coverage\_of\_International\_News\_Contrast\_in\_Narratives\_of\_the\_KAL\_and\_Iran\_Air\_Incidents},
  langid = {english},
  annotation = {Read\_Status: To Read\\
Read\_Status\_Date: 2025-08-21T12:52:46.303Z},
  file = {C\:\\Users\\nasty\\Zotero\\storage\\GCA7NEIK\\229461133_Framing_US_Coverage_of_International_News_Contrast_in_Narratives_of_the_KAL_and_Iran_.html;C\:\\Users\\nasty\\Zotero\\storage\\HTVRLWFR\\229461133_Framing_US_Coverage_of_International_News_Contrast_in_Narratives_of_the_KAL_and_Iran_.html;C\:\\Users\\nasty\\Zotero\\storage\\WRH2JTMB\\229461133_Framing_US_Coverage_of_International_News_Contrast_in_Narratives_of_the_KAL_and_Iran_.html}
}

@article{frankPredictingPragmaticReasoning2012,
  title = {Predicting {{Pragmatic Reasoning}} in {{Language Games}}},
  author = {Frank, Michael C. and Goodman, Noah D.},
  year = 2012,
  month = may,
  journal = {Science},
  volume = {336},
  number = {6084},
  pages = {998--998},
  issn = {0036-8075, 1095-9203},
  doi = {10.1126/science.1218633},
  urldate = {2024-10-01},
  abstract = {Who's Who                            Different languages rely on distinct sets of terminology to classify relatives, such as maternal grandfather in English, and precision in language usage is a key component for successful communication (see the Perspective by                                Levinson                              ).               Kemp and Regier               (p.               1049               ) propose an organizing framework whereby kinship classification systems can all be seen to optimize or nearly optimize both simplicity and precision. The labels applied to kin are constructed from simple units and are precise enough to reduce confusion and ambiguity when used in communication.               Frank and Goodman               (p.               998               ) show that simplicity and precision also explain how listeners correctly infer the meaning of speech in the context of referential communication.                        ,              A Bayesian inference model predicts how listeners decode communications.           ,              One of the most astonishing features of human language is its capacity to convey information efficiently in context. Many theories provide informal accounts of communicative inference, yet there have been few successes in making precise, quantitative predictions about pragmatic reasoning. We examined judgments about simple referential communication games, modeling behavior in these games by assuming that speakers attempt to be informative and that listeners use Bayesian inference to recover speakers' intended referents. Our model provides a close, parameter-free fit to human judgments, suggesting that the use of information-theoretic tools to predict pragmatic reasoning may lead to more effective formal models of communication.},
  langid = {english},
  annotation = {Read\_Status: Not Reading\\
Read\_Status\_Date: 2025-07-28T12:29:43.896Z},
  file = {C\:\\Users\\nasty\\Documents\\Zotero\\Frank and Goodman - 2012 - Predicting Pragmatic Reasoning in Language Games 2.pdf;C\:\\Users\\User\\OneDrive - Universität des Saarlandes\\Documents\\Zotero\\Frank and Goodman - 2012 - Predicting Pragmatic Reasoning in Language Games.pdf}
}

@article{futrellInformationTheoryBridge2022,
  title = {Information {{Theory}} as a {{Bridge Between Language Function}} and {{Language Form}}},
  author = {Futrell, Richard and Hahn, Michael},
  year = 2022,
  month = may,
  journal = {Frontiers in Communication},
  volume = {7},
  publisher = {Frontiers},
  issn = {2297-900X},
  doi = {10.3389/fcomm.2022.657725},
  urldate = {2024-10-01},
  abstract = {{$<$}p{$>$}Formal and functional theories of language seem disparate, because formal theories answer the question of what a language is, while functional theories answer the question of what functions it serves. We argue that information theory provides a bridge between these two approaches, {$<$}italic{$>$}via{$<$}/italic{$>$} a principle of minimization of complexity under constraints. Synthesizing recent work, we show how information-theoretic characterizations of functional complexity lead directly to mathematical descriptions of the forms of possible languages, in terms of solutions to constrained optimization problems. We show how certain linguistic descriptive formalisms can be recovered as solutions to such problems. Furthermore, we argue that information theory lets us define complexity in a way which has minimal dependence on the choice of theory or descriptive formalism. We illustrate this principle using recently-obtained results on universals of word and morpheme order.{$<$}/p{$>$}},
  langid = {english},
  keywords = {Complexity,Information Theory,Language,Linguistic theory,Psycholinguistics},
  annotation = {Read\_Status: Read\\
Read\_Status\_Date: 2025-07-28T13:51:24.198Z},
  file = {C\:\\Users\\nasty\\Documents\\Zotero\\Futrell and Hahn - 2022 - Information Theory as a Bridge Between Language Function and Language Form.pdf;C\:\\Users\\nasty\\Documents\\Zotero\\Futrell and Hahn - 2022 - Information Theory as a Bridge Between Language Function and Language Form.pdf}
}

@article{gargWordEmbeddingsQuantify2018,
  title = {Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypes},
  author = {Garg, Nikhil and Schiebinger, Londa and Jurafsky, Dan and Zou, James},
  year = 2018,
  month = apr,
  journal = {Proceedings of the National Academy of Sciences},
  volume = {115},
  number = {16},
  pages = {E3635-E3644},
  publisher = {Proceedings of the National Academy of Sciences},
  doi = {10.1073/pnas.1720347115},
  urldate = {2024-11-18},
  abstract = {Word embeddings are a powerful machine-learning framework that represents each English word by a vector. The geometric relationship between these vectors captures meaningful semantic relationships between the corresponding words. In this paper, we develop a framework to demonstrate how the temporal dynamics of the embedding helps to quantify changes in stereotypes and attitudes toward women and ethnic minorities in the 20th and 21st centuries in the United States. We integrate word embeddings trained on 100 y of text data with the US Census to show that changes in the embedding track closely with demographic and occupation shifts over time. The embedding captures societal shifts---e.g., the women's movement in the 1960s and Asian immigration into the United States---and also illuminates how specific adjectives and occupations became more closely associated with certain populations over time. Our framework for temporal analysis of word embedding opens up a fruitful intersection between machine learning and quantitative social science.},
  annotation = {Read\_Status: Read\\
Read\_Status\_Date: 2025-07-28T15:54:53.067Z},
  file = {C:\Users\nasty\Documents\Zotero\Garg et al. - 2018 - Word embeddings quantify 100 years of gender and ethnic stereotypes.pdf}
}

@inproceedings{garisolerOneWordTwo2022,
  title = {One {{Word}}, {{Two Sides}}: {{Traces}} of {{Stance}} in {{Contextualized Word Representations}}},
  shorttitle = {One {{Word}}, {{Two Sides}}},
  booktitle = {Proceedings of the 29th {{International Conference}} on {{Computational Linguistics}}},
  author = {Gar{\'i} Soler, Aina and Labeau, Matthieu and Clavel, Chlo{\'e}},
  editor = {Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon},
  year = 2022,
  month = oct,
  pages = {3950--3959},
  publisher = {International Committee on Computational Linguistics},
  address = {Gyeongju, Republic of Korea},
  urldate = {2025-01-23},
  abstract = {The way we use words is influenced by our opinion. We investigate whether this is reflected in contextualized word embeddings. For example, is the representation of ``animal'' different between people who would abolish zoos and those who would not? We explore this question from a Lexical Semantic Change standpoint. Our experiments with BERT embeddings derived from datasets with stance annotations reveal small but significant differences in word representations between opposing stances.},
  annotation = {Read\_Status: To Read\\
Read\_Status\_Date: 2025-07-28T15:55:39.226Z},
  file = {C:\Users\nasty\Documents\Zotero\Garí Soler et al. - 2022 - One Word, Two Sides Traces of Stance in Contextualized Word Representations.pdf}
}

@book{geeraertsLexicalVariationChange2023,
  title = {Lexical {{Variation}} and {{Change}}: {{A Distributional Semantic Approach}}},
  shorttitle = {Lexical {{Variation}} and {{Change}}},
  author = {Geeraerts, Dirk and Speelman, Dirk and Heylen, Kris and Montes, Mariana and De Pascale, Stefano and Franco, Karlien and Lang, Michael},
  year = 2023,
  month = nov,
  edition = {1},
  publisher = {Oxford University PressOxford},
  doi = {10.1093/oso/9780198890676.001.0001},
  urldate = {2024-12-13},
  abstract = {Abstract             Distributional semantics embodies the idea that the context in which a word occurs reveals the meaning of that word. In contemporary corpus linguistics, that idea takes shape in various types of quantitative context analysis. This monograph explores how count-based token-level semantic vector spaces, as an advanced form of such a quantitative methodology, can be applied to the study of polysemy, lexical variation, and lectometry. What can distributional models reveal about meaning? How can they be used to analyse the semantic relationship between near-synonyms? And how can they contribute to the study of lexical variation as a sociolinguistic variable? The book details the conceptual background of lexical semantic and lexical variation research, explains the mechanism of distributional modelling, and introduces distributional workflows and corpus linguistic tools to answer the questions. Combining a cognitive linguistic interest in meaning with a sociolinguistic interest in variation, it illustrates that distributional methodology with case studies on Dutch and Spanish lexical data, focusing on the value of distributional models for semantic analysis, the interaction of semasiological and onomasiological change, and sociolinguistic issues of lexical standardization and pluricentricity.},
  copyright = {https://creativecommons.org/licenses/by-nc-nd/4.0/},
  isbn = {978-0-19-889067-6 978-0-19-199599-6},
  langid = {english},
  annotation = {Read\_Status: To Read\\
Read\_Status\_Date: 2025-07-09T09:59:27.994Z},
  file = {C\:\\Users\\nasty\\Documents\\Zotero\\Geeraerts et al. - 2023 - Lexical Variation and Change A Distributional Semantic Approach 2.pdf;C\:\\Users\\nasty\\Documents\\Zotero\\Geeraerts et al. - 2023 - Lexical Variation and Change A Distributional Semantic Approach.pdf}
}

@inbook{geeraertsSemanticChangeWhat2020,
  title = {Semantic {{Change}}: ``{{What The Smurf}}?''},
  shorttitle = {Semantic {{Change}}},
  booktitle = {The {{Wiley Blackwell Companion}} to {{Semantics}}},
  year = 2020,
  month = nov,
  edition = {1},
  pages = {1--24},
  publisher = {Wiley},
  doi = {10.1002/9781118788516.sem042},
  urldate = {2025-07-23},
  abstract = {This chapter covers the main mechanisms of semantic change, with a focus on word meaning. After introducing the distinction between semasiological and onomasiological, and that between referential and semantic change, processes of change are classified at three different levels: the level of senses, of words, and of the vocabulary as a whole. The chapter rounds off with a discussion of how contemporary corpus linguistics provides a new impetus for diachronic semantics.},
  collaborator = {Geeraerts, Dirk},
  copyright = {http://doi.wiley.com/10.1002/tdm\_license\_1.1},
  isbn = {978-1-118-78831-8 978-1-118-78851-6},
  langid = {english},
  keywords = {camp3_helsinki},
  annotation = {Read\_Status: Read\\
Read\_Status\_Date: 2025-08-18T15:15:58.455Z},
  file = {C:\Users\nasty\Documents\Zotero\2020 - Semantic Change “What The Smurf”.pdf}
}

@article{gentnerINTERESTINGDIFFERENCESVERBS,
  title = {{{SOME INTERESTING DIFFERENCES BETWEEN VERBS AND NOUNS}}},
  author = {Gentner, Dedre},
  langid = {english},
  annotation = {Read\_Status: Not Reading\\
Read\_Status\_Date: 2025-07-28T13:55:32.582Z},
  file = {C:\Users\nasty\Documents\Zotero\Gentner - SOME INTERESTING DIFFERENCES BETWEEN VERBS AND NOUNS.pdf}
}

@article{gerardFearLoathingFrontline2025,
  title = {Fear and {{Loathing}} on the {{Frontline}}: {{Decoding}} the {{Language}} of {{Othering}} by {{Russia-Ukraine War Bloggers}}},
  shorttitle = {Fear and {{Loathing}} on the {{Frontline}}},
  author = {Gerard, Patrick and Weninger, Tim and Lerman, Kristina},
  year = 2025,
  month = jun,
  journal = {Proceedings of the International AAAI Conference on Web and Social Media},
  volume = {19},
  pages = {615--635},
  issn = {2334-0770},
  doi = {10.1609/icwsm.v19i1.35835},
  urldate = {2025-07-01},
  abstract = {Othering---the process of portraying an outgroup as fundamentally different and inferior---often escalates into framing the outgroup as an existential threat, thereby legitimizing exclusion and violence. Throughout history, othering has played a central role in conflicts, from genocides in Nazi Germany and Rwanda to contemporary hostility toward migrants in the US and Europe. Traditional computational methods, such as those used for hate speech detection, frequently overlook the subtle, context-dependent nature of othering language, limiting their effectiveness in real-time detection and analysis. Our work addresses these limitations through three key contributions: (1) a computational framework that combines sociological theory with large language models (LLMs) to identify and analyze othering language, (2) an in-depth examination of othering discourse dynamics, focusing on attention patterns and its interplay with moral framing, and (3) a rapid domain adaptation  enabling robust analysis across different platforms and contexts. We apply our framework to a large corpus of Telegram messages from Russo-Ukrainian war bloggers and political discourse on Gab, revealing several previously unquantified patterns: othering rhetoric surges during crises, often intertwines with moralized language, and escalates during critical periods. Our findings demonstrate that this approach not only surpasses existing hate and fear speech detection methods but also offers actionable insights for anticipating and mitigating threats to social cohesion in conflict-prone environments.},
  copyright = {Copyright (c) 2025 Association for the Advancement of Artificial Intelligence},
  langid = {english},
  keywords = {icwsm2025},
  annotation = {Read\_Status: Read\\
Read\_Status\_Date: 2025-10-06T13:38:17.463Z},
  file = {C:\Users\nasty\Documents\Zotero\Gerard et al. - 2025 - Fear and Loathing on the Frontline Decoding the Language of Othering by Russia-Ukraine War Bloggers.pdf}
}

@article{gerardModelingInformationNarrative2025,
  title = {Modeling {{Information Narrative Evolution}} on {{Telegram During}} the {{Russia-Ukraine War}}},
  author = {Gerard, Patrick and Volkova, Svitlana and Penafiel, Louis and Lerman, Kristina and Weninger, Tim},
  year = 2025,
  month = jun,
  journal = {Proceedings of the International AAAI Conference on Web and Social Media},
  volume = {19},
  pages = {602--614},
  issn = {2334-0770},
  doi = {10.1609/icwsm.v19i1.35834},
  urldate = {2025-09-25},
  abstract = {Following the Russian Federation's full-scale invasion of Ukraine in February 2022, a multitude of information narratives emerged within both pro-Russian and pro-Ukrainian communities online. As the conflict progresses, so too do the information narratives, constantly adapting and influencing local and global community perceptions and attitudes. This dynamic nature of the evolving information environment (IE)  underscores a critical need to fully  discern how narratives evolve and affect online communities. Existing research, however, often fails to capture information narrative evolution, overlooking both the fluid nature of narratives and the internal mechanisms that drive their evolution. Recognizing this, we introduce a novel approach designed to both model narrative evolution and uncover the underlying mechanisms driving them. In this work we perform a comparative discourse analysis across communities on Telegram covering the initial three months following the invasion. First, we uncover substantial disparities in narratives and perceptions between pro-Russian and pro-Ukrainian  communities. Then, we probe deeper into prevalent narratives of each group, identifying key themes and examining the underlying mechanisms fueling their evolution. Finally, we explore influences and factors that may shape the development and spread of narratives.},
  copyright = {Copyright (c) 2025 Association for the Advancement of Artificial Intelligence},
  langid = {english},
  keywords = {icwsm2025},
  annotation = {Read\_Status: Read\\
Read\_Status\_Date: 2025-09-29T11:32:22.543Z},
  file = {C:\Users\nasty\Documents\Zotero\Gerard et al. - 2025 - Modeling Information Narrative Evolution on Telegram During the Russia-Ukraine War.pdf}
}

@inproceedings{gergelSurprisalSatisfactionInformationtheoretic2017,
  title = {Surprisal and {{Satisfaction}}: {{Towards}} an {{Information-theoretic Characterization}} of {{Presuppositions}} with a {{Diachronic Application}}},
  shorttitle = {Surprisal and {{Satisfaction}}},
  booktitle = {Proceedings of the 12th {{International Conference}} on {{Computational Semantics}} ({{IWCS}}) --- {{Short}} Papers},
  author = {Gergel, Remus and {Kopf-Giammanco}, Martin and Masloh, Julia},
  editor = {Gardent, Claire and Retor{\'e}, Christian},
  year = 2017,
  urldate = {2024-12-16},
  annotation = {Read\_Status: Not Reading\\
Read\_Status\_Date: 2025-07-28T13:59:55.707Z},
  file = {C:\Users\nasty\Documents\Zotero\Gergel et al. - 2017 - Surprisal and Satisfaction Towards an Information-theoretic Characterization of Presuppositions wit.pdf}
}

@article{gibsonHowEfficiencyShapes2019,
  title = {How {{Efficiency Shapes Human Language}}},
  author = {Gibson, Edward and Futrell, Richard and Piantadosi, Steven P. and Dautriche, Isabelle and Mahowald, Kyle and Bergen, Leon and Levy, Roger},
  year = 2019,
  month = may,
  journal = {Trends in Cognitive Sciences},
  volume = {23},
  number = {5},
  pages = {389--407},
  issn = {1364-6613},
  doi = {10.1016/j.tics.2019.02.003},
  urldate = {2024-10-01},
  abstract = {Cognitive science applies diverse tools and perspectives to study human language. Recently, an exciting body of work has examined linguistic phenomena through the lens of efficiency in usage: what otherwise puzzling features of language find explanation in formal accounts of how language might be optimized for communication and learning? Here, we review studies that deploy formal tools from probability and information theory to understand how and why language works the way that it does, focusing on phenomena ranging from the lexicon through syntax. These studies show how a pervasive pressure for efficiency guides the forms of natural language and indicate that a rich future for language research lies in connecting linguistics to cognitive psychology and mathematical theories of communication and inference.},
  keywords = {communication,cross-linguistic universals,language complexity,language efficiency,language evolution,language learnability},
  annotation = {Read\_Status: Read\\
Read\_Status\_Date: 2025-07-28T14:02:19.208Z},
  file = {C\:\\Users\\nasty\\Documents\\Zotero\\Gibson et al. - 2019 - How Efficiency Shapes Human Language 1.pdf;C\:\\Users\\nasty\\Documents\\Zotero\\Gibson et al. - 2019 - How Efficiency Shapes Human Language.pdf;C\:\\Users\\User\\OneDrive - Universität des Saarlandes\\Documents\\Zotero\\Gibson et al. - 2019 - How Efficiency Shapes Human Language.pdf}
}

@inproceedings{giulianelliAnalysingLexicalSemantic2020,
  title = {Analysing {{Lexical Semantic Change}} with {{Contextualised Word Representations}}},
  booktitle = {Proceedings of the 58th {{Annual Meeting}} of the {{Association}} for {{Computational Linguistics}}},
  author = {Giulianelli, Mario and Del Tredici, Marco and Fern{\'a}ndez, Raquel},
  year = 2020,
  pages = {3960--3973},
  publisher = {Association for Computational Linguistics},
  address = {Online},
  doi = {10.18653/v1/2020.acl-main.365},
  urldate = {2024-12-16},
  abstract = {This paper presents the first unsupervised approach to lexical semantic change that makes use of contextualised word representations. We propose a novel method that exploits the BERT neural language model to obtain representations of word usages, clusters these representations into usage types, and measures change along time with three proposed metrics. We create a new evaluation dataset and show that the model representations and the detected semantic shifts are positively correlated with human judgements. Our extensive qualitative analysis demonstrates that our method captures a variety of synchronic and diachronic linguistic phenomena. We expect our work to inspire further research in this direction.},
  langid = {english},
  annotation = {Read\_Status: To Read\\
Read\_Status\_Date: 2025-07-28T14:02:59.540Z},
  file = {C:\Users\nasty\Documents\Zotero\Giulianelli et al. - 2020 - Analysing Lexical Semantic Change with Contextualised Word Representations 1.pdf}
}

@misc{glocknerGroundingFallaciesMisrepresenting2025,
  title = {Grounding {{Fallacies Misrepresenting Scientific Publications}} in {{Evidence}}},
  author = {Glockner, Max and Hou, Yufang and Nakov, Preslav and Gurevych, Iryna},
  year = 2025,
  month = feb,
  number = {arXiv:2408.12812},
  eprint = {2408.12812},
  primaryclass = {cs},
  publisher = {arXiv},
  doi = {10.48550/arXiv.2408.12812},
  urldate = {2025-08-13},
  abstract = {Health-related misinformation claims often falsely cite a credible biomedical publication as evidence. These publications only superficially seem to support the false claim, when logical fallacies are applied. In this work, we aim to detect and to highlight such fallacies, which requires assessing the exact content of the misrepresented publications. To achieve this, we introduce MissciPlus, an extension of the fallacy detection dataset Missci. MissciPlus extends Missci by grounding the applied fallacies in real-world passages from misrepresented studies. This creates a realistic test-bed for detecting and verbalizing fallacies under real-world input conditions, and enables new and realistic passage-retrieval tasks. MissciPlus is the first logical fallacy dataset which pairs the real-world misrepresented evidence with incorrect claims, identical to the input to evidence-based fact-checking models. With MissciPlus, we i) benchmark retrieval models in identifying passages that support claims only with fallacious reasoning, ii) evaluate how well LLMs verbalize fallacious reasoning based on misrepresented scientific passages, and iii) assess the effectiveness of fact-checking models in refuting claims that misrepresent biomedical research. Our findings show that current fact-checking models struggle to use misrepresented scientific passages to refute misinformation. Moreover, these passages can mislead LLMs into accepting false claims as true.},
  archiveprefix = {arXiv},
  keywords = {Computer Science - Computation and Language},
  annotation = {Read\_Status: Not Reading\\
Read\_Status\_Date: 2025-10-29T13:00:41.493Z},
  file = {C\:\\Users\\nasty\\Documents\\Zotero\\Glockner et al. - 2025 - Grounding Fallacies Misrepresenting Scientific Publications in Evidence.pdf;C\:\\Users\\nasty\\Zotero\\storage\\23BYS66H\\2408.html}
}

@inproceedings{goelSocialDynamicsLanguage2016,
  title = {The {{Social Dynamics}} of {{Language Change}} in {{Online Networks}}},
  booktitle = {Social {{Informatics}}},
  author = {Goel, Rahul and Soni, Sandeep and Goyal, Naman and Paparrizos, John and Wallach, Hanna and Diaz, Fernando and Eisenstein, Jacob},
  editor = {Spiro, Emma and Ahn, Yong-Yeol},
  year = 2016,
  pages = {41--57},
  publisher = {Springer International Publishing},
  address = {Cham},
  doi = {10.1007/978-3-319-47880-7_3},
  abstract = {Language change is a complex social phenomenon, revealing pathways of communication and sociocultural influence. But, while language change has long been a topic of study in sociolinguistics, traditional linguistic research methods rely on circumstantial evidence, estimating the direction of change from differences between older and younger speakers. In this paper, we use a data set of several million Twitter users to track language changes in progress. First, we show that language change can be viewed as a form of social influence: we observe complex contagion for phonetic spellings and ``netspeak'' abbreviations (e.g., lol), but not for older dialect markers from spoken language. Next, we test whether specific types of social network connections are more influential than others, using a parametric Hawkes process model. We find that tie strength plays an important role: densely embedded social ties are significantly better conduits of linguistic influence. Geographic locality appears to play a more limited role: we find relatively little evidence to support the hypothesis that individuals are more influenced by geographically local social ties, even in their usage of geographical dialect markers.},
  isbn = {978-3-319-47880-7},
  langid = {english},
  keywords = {Base Intensity,Hawkes Process,Inhomogeneous Poisson Process,Language Change,Mutual Friend},
  annotation = {Read\_Status: To Read\\
Read\_Status\_Date: 2025-07-28T15:56:48.327Z},
  file = {C:\Users\nasty\Documents\Zotero\Goel et al. - 2016 - The Social Dynamics of Language Change in Online Networks.pdf}
}

@article{golovchenkoStateMediaCivil2018,
  title = {State, Media and Civil Society in the Information Warfare over {{Ukraine}}: Citizen Curators of Digital Disinformation},
  shorttitle = {State, Media and Civil Society in the Information Warfare over {{Ukraine}}},
  author = {Golovchenko, Yevgeniy and Hartmann, Mareike and {Adler-Nissen}, Rebecca},
  year = 2018,
  month = sep,
  journal = {International Affairs},
  volume = {94},
  number = {5},
  pages = {975--994},
  issn = {0020-5850},
  doi = {10.1093/ia/iiy148},
  urldate = {2025-09-29},
  abstract = {This article explores the dynamics of digital (dis)information in the conflict between Russia and Ukraine. International Relations scholars have presented the online debate in terms of `information warfare'---that is, a number of strategic campaigns to win over local and global public opinion, largely orchestrated by the Kremlin and pro-western authorities. However, this way of describing the online debate reduces civil society to a mere target for manipulation. This article presents a different understanding of the debate. By examining the social media engagement generated by one of the conflict's most important events---the downing of the Malaysian Airlines Flight 17 (MH17) over Ukraine---we explore how competing claims about the cause of the plane crash are disseminated by the state, media and civil society. By analysing approximately 950,000 tweets, the article demonstrates how individual citizens are more than purveyors of government messages; they are the most active drivers of both disinformation and attempts to counter such information. These citizen curators actively shape competing narratives about why MH17 crashed and citizens, as a group, are four times more likely to be retweeted than any other type of user. Our findings challenge conceptualizations of a state-orchestrated information war over Ukraine, and point to the importance of citizen activity in the struggle over truths during international conflicts.},
  annotation = {Read\_Status: Read\\
Read\_Status\_Date: 2025-11-10T14:41:50.217Z},
  file = {C\:\\Users\\nasty\\Documents\\Zotero\\Golovchenko et al. - 2018 - State, media and civil society in the information warfare over Ukraine citizen curators of digital.pdf;C\:\\Users\\nasty\\Zotero\\storage\\B53GEXS4\\iiy148.html}
}

@article{greeneHotTweetsCold2025,
  title = {Hot {{Tweets}} and {{Cold Posts}}: {{Variation}} in {{US Congresspeople}}'s {{Ideological Presentation}} on {{Twitter}} and {{Facebook Over Time}}},
  shorttitle = {Hot {{Tweets}} and {{Cold Posts}}},
  author = {Greene, Kevin T. and DeVerna, Matthew R. and Tucker, Joshua A. and Buntain, Cody},
  year = 2025,
  month = jun,
  journal = {Proceedings of the International AAAI Conference on Web and Social Media},
  volume = {19},
  pages = {717--730},
  issn = {2334-0770},
  doi = {10.1609/icwsm.v19i1.35841},
  urldate = {2025-07-01},
  abstract = {This work presents a novel observational study of US congresspeople's link-based news-sharing behaviors and ideological presentations across Facebook and Twitter. By analyzing the web domains these politicians share, we estimate their political ideologies and measure ideological extremity across the political and social contexts of these platforms. Our findings show that these politicians present as more ideologically extreme on Facebook than they appear on Twitter, particularly among Democrats. However, this difference is relatively small compared to the ideological shift between a politician's publicly funded official account and their campaign account---a shift that is roughly seven times larger. Finally, we observe that these changes are not uniform over time across parties; expressed polarization within the Democratic Party notably increased from 2013 to 2017 before stabilizing, while the Republican Party became markedly more polarized starting in 2020. While more research is needed to identify the specific affordances that contribute to more expressed polarization on Facebook and potential temporal dynamics between these platforms, this work highlights the limitations of studies that focus on single platforms and opens new avenues for future research into how differences across online social spaces may impact political polarization.},
  copyright = {Copyright (c) 2025 Association for the Advancement of Artificial Intelligence},
  langid = {english},
  keywords = {icwsm2025},
  annotation = {Read\_Status: To Read\\
Read\_Status\_Date: 2025-07-03T14:51:25.294Z},
  file = {C:\Users\nasty\Documents\Zotero\Greene et al. - 2025 - Hot Tweets and Cold Posts Variation in US Congresspeople's Ideological Presentation on Twitter and.pdf}
}

@inproceedings{guptaSociodemographicBiasLanguage2024,
  title = {Sociodemographic {{Bias}} in {{Language Models}}: {{A Survey}} and {{Forward Path}}},
  shorttitle = {Sociodemographic {{Bias}} in {{Language Models}}},
  booktitle = {Proceedings of the 5th {{Workshop}} on {{Gender Bias}} in {{Natural Language Processing}} ({{GeBNLP}})},
  author = {Gupta, Vipul and Narayanan Venkit, Pranav and Wilson, Shomir and Passonneau, Rebecca},
  editor = {Fale{\'n}ska, Agnieszka and Basta, Christine and {Costa-juss{\`a}}, Marta and {Goldfarb-Tarrant}, Seraphina and Nozza, Debora},
  year = 2024,
  month = aug,
  pages = {295--322},
  publisher = {Association for Computational Linguistics},
  address = {Bangkok, Thailand},
  doi = {10.18653/v1/2024.gebnlp-1.19},
  urldate = {2025-03-27},
  abstract = {Sociodemographic bias in language models (LMs) has the potential for harm when deployed in real-world settings. This paper presents a comprehensive survey of the past decade of research on sociodemographic bias in LMs, organized into a typology that facilitates examining the different aims: types of bias, quantifying bias, and debiasing techniques. We track the evolution of the latter two questions, then identify current trends and their limitations, as well as emerging techniques. To guide future research towards more effective and reliable solutions, and to help authors situate their work within this broad landscape, we conclude with a checklist of open questions.},
  annotation = {Read\_Status: Not Reading\\
Read\_Status\_Date: 2025-07-28T15:41:38.739Z},
  file = {C:\Users\User\OneDrive - Universität des Saarlandes\Documents\Zotero\Gupta et al. - 2024 - Sociodemographic Bias in Language Models A Survey and Forward Path.pdf}
}

@inproceedings{haleProbabilisticEarleyParser2001,
  title = {A Probabilistic Earley Parser as a Psycholinguistic Model},
  booktitle = {Proceedings of the Second Meeting of the {{North American Chapter}} of the {{Association}} for {{Computational Linguistics}} on {{Language}} Technologies},
  author = {Hale, John},
  year = 2001,
  month = jun,
  series = {{{NAACL}} '01},
  pages = {1--8},
  publisher = {Association for Computational Linguistics},
  address = {USA},
  doi = {10.3115/1073336.1073357},
  urldate = {2025-03-12},
  abstract = {In human sentence processing, cognitive load can be defined many ways. This report considers a definition of cognitive load in terms of the total probability of structural options that have been disconfirmed at some point in a sentence: the surprisal of word wi given its prefix wo...i-1 on a phrase-structural language model. These loads can be efficiently calculated using a probabilistic Earley parser (Stolcke, 1995) which is interpreted as generating predictions about reading time on a word-by-word basis. Under grammatical assumptions supported by corpus-frequency data, the operation of Stolcke's probabilistic Earley parser correctly predicts processing phenomena associated with garden path structural ambiguity and with the subject/object relative asymmetry.},
  annotation = {Read\_Status: Not Reading\\
Read\_Status\_Date: 2025-07-28T14:06:40.012Z},
  file = {C:\Users\nasty\Documents\Zotero\Hale - 2001 - A probabilistic earley parser as a psycholinguistic model 2.pdf}
}

@inproceedings{hamiltonCulturalShiftLinguistic2016,
  title = {Cultural {{Shift}} or {{Linguistic Drift}}? {{Comparing Two Computational Measures}} of {{Semantic Change}}},
  shorttitle = {Cultural {{Shift}} or {{Linguistic Drift}}?},
  booktitle = {Proceedings of the 2016 {{Conference}} on {{Empirical Methods}} in {{Natural Language Processing}}},
  author = {Hamilton, William L. and Leskovec, Jure and Jurafsky, Dan},
  editor = {Su, Jian and Duh, Kevin and Carreras, Xavier},
  year = 2016,
  month = nov,
  pages = {2116--2121},
  publisher = {Association for Computational Linguistics},
  address = {Austin, Texas},
  doi = {10.18653/v1/D16-1229},
  urldate = {2024-11-21},
  annotation = {Read\_Status: To Read\\
Read\_Status\_Date: 2025-07-28T14:07:40.639Z},
  file = {C:\Users\nasty\Documents\Zotero\Hamilton et al. - 2016 - Cultural Shift or Linguistic Drift Comparing Two Computational Measures of Semantic Change 1.pdf}
}

@inproceedings{hamiltonDiachronicWordEmbeddings2016,
  title = {Diachronic {{Word Embeddings Reveal Statistical Laws}} of {{Semantic Change}}},
  booktitle = {Proceedings of the 54th {{Annual Meeting}} of the {{Association}} for {{Computational Linguistics}} ({{Volume}} 1: {{Long Papers}})},
  author = {Hamilton, William L. and Leskovec, Jure and Jurafsky, Dan},
  editor = {Erk, Katrin and Smith, Noah A.},
  year = 2016,
  month = aug,
  pages = {1489--1501},
  publisher = {Association for Computational Linguistics},
  address = {Berlin, Germany},
  doi = {10.18653/v1/P16-1141},
  urldate = {2024-11-07},
  annotation = {Read\_Status: Read\\
Read\_Status\_Date: 2025-07-28T14:07:03.201Z},
  file = {C\:\\Users\\nasty\\Documents\\Zotero\\Hamilton et al. - 2016 - Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change 1.pdf;C\:\\Users\\nasty\\Documents\\Zotero\\Hamilton et al. - 2016 - Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change 2.pdf;C\:\\Users\\User\\OneDrive - Universität des Saarlandes\\Documents\\Zotero\\Hamilton et al. - 2016 - Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change.pdf}
}

@inproceedings{hardalovFewshotCrosslingualStance2022,
  title = {Few-Shot Cross-Lingual Stance Detection with Sentiment-Based Pre-Training},
  booktitle = {Proceedings of the {{AAAI Conference}} on {{Artificial Intelligence}}},
  author = {Hardalov, Momchil and Arora, Arnav and Nakov, Preslav and Augenstein, Isabelle},
  year = 2022,
  volume = {36},
  pages = {10729--10737},
  urldate = {2024-11-21},
  annotation = {Read\_Status: Not Reading\\
Read\_Status\_Date: 2025-07-28T14:09:21.205Z},
  file = {C:\Users\nasty\Documents\Zotero\Hardalov et al. - 2022 - Few-shot cross-lingual stance detection with sentiment-based pre-training 1.pdf}
}

@misc{hardalovSurveyStanceDetection2022,
  title = {A {{Survey}} on {{Stance Detection}} for {{Mis-}} and {{Disinformation Identification}}},
  author = {Hardalov, Momchil and Arora, Arnav and Nakov, Preslav and Augenstein, Isabelle},
  year = 2022,
  month = may,
  number = {arXiv:2103.00242},
  eprint = {2103.00242},
  publisher = {arXiv},
  doi = {10.48550/arXiv.2103.00242},
  urldate = {2024-11-21},
  abstract = {Understanding attitudes expressed in texts, also known as stance detection, plays an important role in systems for detecting false information online, be it misinformation (unintentionally false) or disinformation (intentionally false information). Stance detection has been framed in different ways, including (a) as a component of fact-checking, rumour detection, and detecting previously fact-checked claims, or (b) as a task in its own right. While there have been prior efforts to contrast stance detection with other related tasks such as argumentation mining and sentiment analysis, there is no existing survey on examining the relationship between stance detection and mis- and disinformation detection. Here, we aim to bridge this gap by reviewing and analysing existing work in this area, with mis- and disinformation in focus, and discussing lessons learnt and future challenges.},
  archiveprefix = {arXiv},
  keywords = {Computer Science - Computation and Language,Computer Science - Social and Information Networks},
  annotation = {Read\_Status: To Read\\
Read\_Status\_Date: 2025-07-28T14:09:38.838Z},
  file = {C\:\\Users\\nasty\\Documents\\Zotero\\Hardalov et al. - 2022 - A Survey on Stance Detection for Mis- and Disinformation Identification 1.pdf;C\:\\Users\\nasty\\Zotero\\storage\\G86I759W\\2103.html}
}

@misc{hartmannMappingDisInformationFlow2019,
  title = {Mapping ({{Dis-}}){{Information Flow}} about the {{MH17 Plane Crash}}},
  author = {Hartmann, Mareike and Golovchenko, Yevgeniy and Augenstein, Isabelle},
  year = 2019,
  month = oct,
  number = {arXiv:1910.01363},
  eprint = {1910.01363},
  publisher = {arXiv},
  urldate = {2024-11-21},
  abstract = {Digital media enables not only fast sharing of information, but also disinformation. One prominent case of an event leading to circulation of disinformation on social media is the MH17 plane crash. Studies analysing the spread of information about this event on Twitter have focused on small, manually annotated datasets, or used proxys for data annotation. In this work, we examine to what extent text classifiers can be used to label data for subsequent content analysis, in particular we focus on predicting pro-Russian and pro-Ukrainian Twitter content related to the MH17 plane crash. Even though we find that a neural classifier improves over a hashtag based baseline, labeling pro-Russian and pro-Ukrainian content with high precision remains a challenging problem. We provide an error analysis underlining the difficulty of the task and identify factors that might help improve classification in future work. Finally, we show how the classifier can facilitate the annotation task for human annotators.},
  archiveprefix = {arXiv},
  keywords = {Computer Science - Computation and Language,Computer Science - Computers and Society},
  annotation = {Read\_Status: Read\\
Read\_Status\_Date: 2025-07-28T14:10:08.550Z},
  file = {C\:\\Users\\nasty\\Documents\\Zotero\\Hartmann et al. - 2019 - Mapping (Dis-)Information Flow about the MH17 Plane Crash 1.pdf;C\:\\Users\\nasty\\Zotero\\storage\\C66ETKTJ\\1910.html}
}

@phdthesis{heinPropagandaDetectionRussian2023,
  type = {Thesis},
  title = {Propaganda Detection in {{Russian}} and {{American}} News Coverage about the War in {{Ukraine}} through Text Classification},
  author = {Hein, Vitalij},
  year = 2023,
  doi = {10.34726/hss.2023.104640},
  urldate = {2024-12-10},
  abstract = {In der Vergangenheit war Propaganda eine entscheidende Technik, um die Meinungen von Menschen subtil zu manipulieren. Mit dem Aufstieg der Massenmedien k\"onnen subtile Meinungen von verschiedenen Quellen vermittelt werden, ohne dass der Leser dies erkennt. Auf diese Weise kann eine ganze Bev\"olkerung dazu manipuliert werden, bestimmte Aspekte zu glauben. Diese Thesis untersucht das Thema Propaganda in Nachrichtenartikeln. Durch Anwendung der CRISP-DM-Forschungsmethodik analysiert die Studie Propaganda-Techniken im russischen und amerikanischen Nachrichten, mit Fokus auf die Medienberichterstattung \"uber den Angriff auf die Ukraine. Die Forschung pr\"asentiert ein Propaganda Detection Model, das Ensemble Learning verwendet. Das Modell kombiniert mehrere Transformer Models, einschlie\ss lich BERT, OPT und RoBERTa, um 18 Propaganda-Techniken zu erkennen. Eine zentrale Erkenntnis ist die Wirksamkeit von Meta Classification mit Model Stacking, was Ensemble Averaging und andere Methoden \"ubertrifft und einen Micro F1-Score von 0,78 erreicht, was auf ein hohes Ma\ss{} an Genauigkeit bei der Propaganda-Erkennung hindeutet. Die Forschung zeigt das Potenzial des Propaganda Detection Model, Propaganda in Nachrichtenartikeln entgegenzuwirken und damit demokratische Prozesse zu bewahren. Sie tr\"agt zum Verst\"andnis der Nutzung von Propaganda im russischen und amerikanischen Nachrichtenkontext bei, indem sie deren monatliche Nutzung von Januar 2022 bis April 2023 analysiert und die Verteilung der Propaganda-Techniken vergleicht.},
  copyright = {http://rightsstatements.org/vocab/InC/1.0/},
  langid = {english},
  school = {Technische Universit\"at Wien},
  annotation = {Accepted: 2023-09-11T08:56:05Z\\
Read\_Status: Read\\
Read\_Status\_Date: 2025-09-05T11:38:12.420Z},
  file = {C:\Users\nasty\Documents\Zotero\Hein - 2023 - Propaganda detection in Russian and American news coverage about the war in Ukraine through text cla.pdf}
}

@book{hengchenComputationalApproachesSemantic2021,
  title = {Computational Approaches to Semantic Change ({{Volume}} 6)},
  editor = {Hengchen, Simon and Xu, Yang and Tahmasebi, Nina and Jatowt, Adam and Borin, Lars},
  year = 2021,
  publisher = {Language Science Press},
  doi = {10.5281/zenodo.5040241},
  urldate = {2024-11-07},
  abstract = {Semantic change --- how the meanings of words change over time --- has preoccupied scholars since well before modern linguistics emerged in the late 19th and early 20th century, ushering in a new methodological turn in the study of language change. Compared to changes in sound and grammar, semantic change is the least understood. Ever since, the study of semantic change has progressed steadily, accumulating a vast store of knowledge for over a century, encompassing many languages and language families. Historical linguists also early on realized the potential of computers as research tools, with papers at the very first international conferences in computational linguistics in the 1960s. Such computational studies still tended to be small-scale, method-oriented, and qualitative. However, recent years have witnessed a sea-change in this regard. Big-data empirical quantitative investigations are now coming to the forefront, enabled by enormous advances in storage capability and processing power. Diachronic corpora have grown beyond imagination, defying exploration by traditional manual qualitative methods, and language technology has become increasingly data-driven and semantics-oriented. These developments present a golden opportunity for the empirical study of semantic change over both long and short time spans.},
  langid = {english},
  keywords = {Language Arts & Disciplines,Linguistics,thema EDItEUR::C Language and Linguistics::CF Linguistics},
  annotation = {Accepted: 2021-10-16T05:31:20Z\\
Read\_Status: To Read\\
Read\_Status\_Date: 2025-07-28T14:11:06.483Z},
  file = {C\:\\Users\\nasty\\Documents\\Zotero\\Hengchen et al. - 2021 - Computational approaches to semantic change (Volume 6) 2.pdf;C\:\\Users\\User\\OneDrive - Universität des Saarlandes\\Documents\\Zotero\\Hengchen et al. - 2021 - Computational approaches to semantic change (Volume 6).pdf}
}

@inproceedings{hoekenMethodologicalInsightsDetecting2023,
  title = {Methodological {{Insights}} in {{Detecting Subtle Semantic Shifts}} with {{Contextualized}} and {{Static Language Models}}},
  booktitle = {Findings of the {{Association}} for {{Computational Linguistics}}: {{EMNLP}} 2023},
  author = {Hoeken, Sanne and Alacam, {\"O}zge and Fokkens, Antske and Sommerauer, Pia},
  editor = {Bouamor, Houda and Pino, Juan and Bali, Kalika},
  year = 2023,
  month = dec,
  pages = {3662--3675},
  publisher = {Association for Computational Linguistics},
  address = {Singapore},
  doi = {10.18653/v1/2023.findings-emnlp.237},
  urldate = {2024-11-07},
  abstract = {In this paper, we investigate automatic detection of subtle semantic shifts between social communities of different political convictions in Dutch and English. We perform a methodological study comparing methods using static and contextualized language models. We investigate the impact of specializing contextualized models through fine-tuning on target corpora, word sense disambiguation and sentiment. We furthermore propose a new approach using masked token prediction, that relies on behavioral information, specifically the most probable substitutions, instead of geometrical comparison of representations. Our results show that methods using static models and our masked token prediction method can detect differences in connotation of politically loaded terms, whereas methods that rely on measuring the distance between contextualized representations are not providing clear signals, even in synthetic scenarios of extreme shifts.},
  annotation = {Read\_Status: To Read\\
Read\_Status\_Date: 2025-07-28T14:11:38.220Z},
  file = {C\:\\Users\\nasty\\Documents\\Zotero\\Hoeken et al. - 2023 - Methodological Insights in Detecting Subtle Semantic Shifts with Contextualized and Static Language 2.pdf;C\:\\Users\\User\\OneDrive - Universität des Saarlandes\\Documents\\Zotero\\Hoeken et al. - 2023 - Methodological Insights in Detecting Subtle Semantic Shifts with Contextualized and Static Language.pdf}
}

@article{hughesQuantitativePatternsStylistic2012,
  title = {{Quantitative Patterns of Stylistic Influence in the Evolution of Literature}},
  author = {Hughes, James M. and Foti, Nicholas J. and Krakauer, David C. and Rockmore, Daniel N.},
  year = 2012,
  month = may,
  journal = {Proceedings of the National Academy of Sciences},
  volume = {109},
  number = {20},
  pages = {7682--7686},
  publisher = {Proceedings of the National Academy of Sciences},
  doi = {10.1073/pnas.1115407109},
  urldate = {2024-12-10},
  abstract = {Literature is a form of expression whose temporal structure, both in content and style, provides a historical record of the evolution of culture. In this work we take on a quantitative analysis of literary style and conduct the first large-scale temporal stylometric study of literature by using the vast holdings in the Project Gutenberg Digital Library corpus. We find temporal stylistic localization among authors through the analysis of the similarity structure in feature vectors derived from content-free word usage, nonhomogeneous decay rates of stylistic influence, and an accelerating rate of decay of influence among modern authors. Within a given time period we also find evidence for stylistic coherence with a given literary topic, such that writers in different fields adopt different literary styles. This study gives quantitative support to the notion of a literary ``style of a time'' with a strong trend toward increasingly contemporaneous stylistic influence.},
  annotation = {Read\_Status: Not Reading\\
Read\_Status\_Date: 2025-07-28T14:13:55.436Z},
  file = {C:\Users\nasty\Documents\Zotero\Hughes et al. - 2012 - Quantitative patterns of stylistic influence in the evolution of literature 1.pdf}
}

@article{hyzenRevisitingTheoreticalFoundations2021,
  title = {Revisiting the {{Theoretical Foundations}} of {{Propaganda}}},
  author = {Hyzen, Aaron},
  year = 2021,
  month = jul,
  journal = {International Journal of Communication},
  volume = {15},
  number = {0},
  pages = {18},
  issn = {1932-8036},
  urldate = {2025-02-03},
  abstract = {This article revisits the theoretical--philosophical foundations of propaganda to better position it in contemporary conceptual discussions about (computational) dis/mis/malinformation. It discusses propaganda as a tangible expression of ideology in communication---its principal purpose to enforce ideological goals, manage opinion, and consolidate loyalties. Starting from the notion of propaganda as a technique to further ideological interests that naturally hail from it, propaganda is discussed in relation to communication and information and how it relates to ideology and power, referring to ideas from key authors including Marx, Althusser, Gramsci, and Lukes. Taking inspiration from Gramsci, it discusses the role in propaganda communication of intellectuals, operating at the behest of elite power, but increasingly for the intellectuals' own interests. Finally, propaganda, as communication, effecting values + beliefs, and therefore opinion, is analyzed as a central component in creating, influencing, and justifying Searle's notion of status functions in society.},
  copyright = {The  International Journal of Communication  is an academic journal. As such, it is dedicated to the open exchange of information. For this reason, IJoC is freely available to individuals and institutions. Copies of this journal or articles in this journal may be distributed for research or educational purposes free of charge and without permission. However, commercial use of the IJoC website or the articles contained herein is expressly prohibited without the written consent of the editor. Authors who publish in The  International Journal of Communication  will release their articles under the   Creative Commons Attribution Non-Commercial No Derivatives (by-nc-nd) license  . This license allows anyone to copy and distribute the article for non-commercial purposes provided that appropriate attribution is given. For details of the rights authors grants users of their work, see the  "human-readable summary" of the license , with a link to the full license. (Note that "you" refers to a user, not an author, in the summary.) This journal utilizes the  LOCKSS system to create a distributed archiving system among participating libraries and permits those libraries to create permanent archives of the journal for purposes of preservation and restoration. The publisher perpetually authorizes participants in the LOCKSS system to archive and restore our publication through the LOCKSS System for the benefit of all LOCKSS System participants. Specifically participating libraries may:  Collect and preserve currently accessible materials;  Use material consistent with original license terms;  Provide copies to other LOCKSS appliances for purposes of audit and repair.   ~    Fair Use The U.S. Copyright Act of 1976 specifies, in Section 107, the terms of the Fair Use exception: Notwithstanding the provisions of sections 106 and 106A, the fair use of a copyrighted work, including such use by reproduction in copies or phonorecords or by any other means specified by that section, for purposes such as criticism, comment, news reporting, teaching (including multiple copies for classroom use), scholarship, or research, is not an infringement of copyright. In determining whether the use made of a work in any particular case is a fair use the factors to be considered shall include:  the purpose and character of the use, including whether such use is of a commercial nature or is for nonprofit educational purposes;  the nature of the copyrighted work;  the amount and substantiality of the portion used in relation to the copyrighted work as a whole; \&amp;  the effect of the use upon the potential market for or value of the copyrighted work.   The fact that a work is unpublished shall not itself bar a finding of fair use if such finding is made upon consideration of all the above factors. In accord with these provisions, the  International Journal of Communication  believes in the vigorous assertion and defense of Fair Use by scholars engaged in academic research, teaching and non-commercial publishing. Thus, we view the inclusion of ``quotations'' from existing print, visual, audio and audio-visual texts to be appropriate examples of Fair Use, as are reproductions of visual images for the purpose of scholarly analysis. We encourage authors to obtain appropriate permissions to use materials originally produced by others, but do not require such permissions as long as the usage of such materials falls within the boundaries of Fair Use.  The  International Journal of Communication  encourages authors to employ fair use in their scholarly publishing wherever appropriate. Fair use is the right to use unlicensed copyrighted material (whether it is text, images, audio-visual, or other) in your own work, in some circumstances. We consult the  Code of Best Practices in Fair Use for Scholarly Research in Communication , created by the International Communication Association and endorsed by the National Communication Association, and you should too. If you have any questions about whether fair use applies to your uses of copyrighted material (whether it is text, images, audio-visual, or other) in your scholarship, simply include your rationale, grounded in the Best Practices, as a supplementary document with your submission.},
  langid = {english},
  keywords = {computational propaganda,disinformation,ideology,malinformation,misinformation,power,propaganda,propaganda_theory},
  annotation = {Read\_Status: To Read\\
Read\_Status\_Date: 2025-07-03T13:30:41.656Z},
  file = {C:\Users\nasty\Documents\Zotero\Hyzen - 2021 - Revisiting the Theoretical Foundations of Propaganda.pdf}
}

@article{johnsonSourcesContinuedInfluence1994,
  title = {Sources of the Continued Influence Effect: {{When}} Misinformation in Memory Affects Later Inferences},
  shorttitle = {Sources of the Continued Influence Effect},
  author = {Johnson, Hollyn M. and Seifert, Colleen M.},
  year = 1994,
  journal = {Journal of Experimental Psychology: Learning, Memory, and Cognition},
  volume = {20},
  number = {6},
  pages = {1420--1436},
  publisher = {American Psychological Association},
  address = {US},
  issn = {1939-1285},
  doi = {10.1037/0278-7393.20.6.1420},
  abstract = {Several lines of research have found that information previously encoded into memory can influence inferences and judgments, even when more recent information discredits it. Previous theories have attributed this to difficulties in editing memory: failing to successfully trace out and alter inferences or explanations generated before a correction. However, in Exps 1A and 1B, Ss who had received an immediate correction made as many inferences based on misinformation as Ss who had received the correction later in the account (despite presumably having made more inferences requiring editing). In a 2nd experiment, the availability of the misinformation within the comprehension context was tested. Results showed that Ss continued to make inferences involving discredited information when it afforded causal structure, but not when only incidentally mentioned or primed during an intervening task. Exps 3A and 3B found that providing a plausible causal alternative, rather than simply negating misinformation, mitigated 1 effect. The findings suggest that misinformation can still influence inferences one generates after a correction has occurred; however, providing an alternative that replaces the causal structure it affords can reduce the effects of misinformation. (PsycInfo Database Record (c) 2025 APA, all rights reserved)},
  keywords = {Human Information Storage,Inference,Information,Memory,Misinformation},
  annotation = {Read\_Status: New\\
Read\_Status\_Date: 2025-11-28T18:28:54.374Z}
}

@article{joseWhatsLabelPropaganda2025,
  title = {What's in a {{Label}}? {{Propaganda Labels}} and {{User Sharing Behavior}} on {{Social Media Platforms}}},
  shorttitle = {What's in a {{Label}}?},
  author = {Jose, Julia and Geeng, Chris and Morales, Kediel O. and McCoy, Damon and Greenstadt, Rachel},
  year = 2025,
  month = jun,
  journal = {Proceedings of the International AAAI Conference on Web and Social Media},
  volume = {19},
  pages = {918--934},
  issn = {2334-0770},
  doi = {10.1609/icwsm.v19i1.35853},
  urldate = {2025-07-01},
  abstract = {Authentic information is vital for a society's ability to make rational decisions. Fabricated and manipulative information can be harmful to society as seen in cases of threatening events that were consequences of foreign propaganda and radical ideologies. While past research has studied dis- and misinformation on social media platforms, the study of propaganda has received much less attention. This study explores the sharing intentions of propaganda on social media platforms and develops an intervention to help detect it. In a randomized controlled trial setting, we added indicators to social media posts that used propaganda techniques to advance an agenda, including techniques that rely on fallacious reasoning, emotional rather than logical reasoning, etc. We then asked our participants (n=1,187) about their intention to engage with these posts. We found that participants were significantly (2.4 times) less likely to share these posts with indicators. We also found that participants' political affiliation moderated their sharing intentions. We believe our findings provide valuable insights for the study of propaganda on social media platforms.},
  copyright = {Copyright (c) 2025 Association for the Advancement of Artificial Intelligence},
  langid = {english},
  keywords = {icwsm2025},
  annotation = {Read\_Status: To Read\\
Read\_Status\_Date: 2025-07-03T14:51:59.134Z},
  file = {C:\Users\nasty\Documents\Zotero\Jose et al. - 2025 - What’s in a Label Propaganda Labels and User Sharing Behavior on Social Media Platforms.pdf}
}

@misc{kaffeeWhyShouldThis2023,
  title = {Why {{Should This Article Be Deleted}}? {{Transparent Stance Detection}} in {{Multilingual Wikipedia Editor Discussions}}},
  shorttitle = {Why {{Should This Article Be Deleted}}?},
  author = {Kaffee, Lucie-Aim{\'e}e and Arora, Arnav and Augenstein, Isabelle},
  year = 2023,
  month = oct,
  number = {arXiv:2310.05779},
  eprint = {2310.05779},
  publisher = {arXiv},
  urldate = {2024-11-21},
  abstract = {The moderation of content on online platforms is usually non-transparent. On Wikipedia, however, this discussion is carried out publicly and the editors are encouraged to use the content moderation policies as explanations for making moderation decisions. Currently, only a few comments explicitly mention those policies -- 20\% of the English ones, but as few as 2\% of the German and Turkish comments. To aid in this process of understanding how content is moderated, we construct a novel multilingual dataset of Wikipedia editor discussions along with their reasoning in three languages. The dataset contains the stances of the editors (keep, delete, merge, comment), along with the stated reason, and a content moderation policy, for each edit decision. We demonstrate that stance and corresponding reason (policy) can be predicted jointly with a high degree of accuracy, adding transparency to the decision-making process. We release both our joint prediction models and the multilingual content moderation dataset for further research on automated transparent content moderation.},
  archiveprefix = {arXiv},
  keywords = {Computer Science - Computation and Language,Computer Science - Computers and Society,Computer Science - Machine Learning},
  annotation = {Read\_Status: Not Reading\\
Read\_Status\_Date: 2025-07-28T14:15:48.578Z},
  file = {C\:\\Users\\nasty\\Documents\\Zotero\\Kaffee et al. - 2023 - Why Should This Article Be Deleted Transparent Stance Detection in Multilingual Wikipedia Editor Di.pdf;C\:\\Users\\nasty\\Zotero\\storage\\UDCXMYCF\\2310.html}
}

@article{kiddPsychologyNeuroscienceCuriosity2015,
  title = {The Psychology and Neuroscience of Curiosity},
  author = {Kidd, Celeste and Hayden, Benjamin Y.},
  year = 2015,
  month = nov,
  journal = {Neuron},
  volume = {88},
  number = {3},
  pages = {449--460},
  issn = {0896-6273},
  doi = {10.1016/j.neuron.2015.09.010},
  urldate = {2024-10-01},
  abstract = {Curiosity is a basic element of our cognition, yet its biological function, mechanisms, and neural underpinning remain poorly understood. It is nonetheless a motivator for learning, influential in decision-making, and crucial for healthy development. One factor limiting our understanding of it is the lack of a widely agreed upon delineation of what is and is not curiosity; another factor is the dearth of standardized laboratory tasks that manipulate curiosity in the lab. Despite these barriers, recent years have seen a major growth of interest in both the neuroscience and psychology of curiosity. In this Perspective, we advocate for the importance of the field, provide a selective overview of its current state, and describe tasks that are used to study curiosity and information-seeking. We propose that, rather than worry about defining curiosity, it is more helpful to consider the motivations for information-seeking behavior and to study it in its ethological context.},
  pmcid = {PMC4635443},
  pmid = {26539887},
  annotation = {Read\_Status: Read\\
Read\_Status\_Date: 2025-07-28T14:17:53.978Z},
  file = {C\:\\Users\\nasty\\Documents\\Zotero\\Kidd and Hayden - 2015 - The psychology and neuroscience of curiosity.pdf;C\:\\Users\\nasty\\Documents\\Zotero\\Kidd and Hayden - 2015 - The psychology and neuroscience of curiosity.pdf}
}

@article{kilgarriffDontBelieveWord1997,
  title = {I {{Don}}'t {{Believe}} in {{Word Senses}}},
  author = {Kilgarriff, Adam},
  year = 1997,
  journal = {Computers and the Humanities},
  volume = {31},
  number = {2},
  eprint = {30204773},
  eprinttype = {jstor},
  pages = {91--113},
  publisher = {Springer},
  issn = {0010-4817},
  urldate = {2024-12-05},
  abstract = {Word sense disambiguation assumes word senses. Within the lexicography and linguistics literature, they are known to be very slippery entities. The paper looks at problems with existing accounts of 'word sense' and describes the various kinds of ways in which a word's meaning can deviate from its core meaning. An analysis is presented in which word senses are abstractions from clusters of corpus citations, in accordance with current lexicographic practice. The corpus citations, not the word senses, are the basic objects in the ontology. The corpus citations will be clustered into senses according to the purposes of whoever or whatever does the clustering. In the absence of such purposes, word senses do not exist. Word sense disambiguation also needs a set of word senses to disambiguate between. In most recent work, the set has been taken from a general-purpose lexical resource, with the assumption that the lexical resource describes the word senses of English/French/..., between which NLP applications will need to disambiguate. The implication of the paper is, by contrast, that word senses exist only relative to a task.},
  annotation = {Read\_Status: Not Reading\\
Read\_Status\_Date: 2025-07-28T14:20:10.548Z},
  file = {C:\Users\nasty\Documents\Zotero\Kilgarriff - 1997 - I Don't Believe in Word Senses 1.pdf}
}

@article{klingensteinCivilizingProcessLondons2014,
  title = {{The Civilizing Process in {{London}}'s {{Old Bailey}}}},
  author = {Klingenstein, Sara and Hitchcock, Tim and DeDeo, Simon},
  year = 2014,
  month = jul,
  journal = {Proceedings of the National Academy of Sciences of the United States of America},
  volume = {111},
  number = {26},
  pages = {9419--9424},
  issn = {1091-6490},
  doi = {10.1073/pnas.1405984111},
  abstract = {The jury trial is a critical point where the state and its citizens come together to define the limits of acceptable behavior. Here we present a large-scale quantitative analysis of trial transcripts from the Old Bailey that reveal a major transition in the nature of this defining moment. By coarse-graining the spoken word testimony into synonym sets and dividing the trials based on indictment, we demonstrate the emergence of semantically distinct violent and nonviolent trial genres. We show that although in the late 18th century the semantic content of trials for violent offenses is functionally indistinguishable from that for nonviolent ones, a long-term, secular trend drives the system toward increasingly clear distinctions between violent and nonviolent acts. We separate this process into the shifting patterns that drive it, determine the relative effects of bureaucratic change and broader cultural shifts, and identify the synonym sets most responsible for the eventual genre distinguishability. This work provides a new window onto the cultural and institutional changes that accompany the monopolization of violence by the state, described in qualitative historical analysis as the civilizing process.},
  langid = {english},
  pmcid = {PMC4084475},
  pmid = {24979792},
  keywords = {bureaucracy,Crime,Criminal Law,cultural evolution,Cultural Evolution,group cognition,History 18th Century,History 19th Century,History 20th Century,History 21st Century,information theory,London,Social Control Policies,social systems,Violence},
  annotation = {Read\_Status: To Read\\
Read\_Status\_Date: 2025-07-28T14:22:20.317Z},
  file = {C:\Users\nasty\Documents\Zotero\Klingenstein et al. - 2014 - The civilizing process in London's Old Bailey.pdf}
}

@article{kotsevaTrendAnalysisCOVID192023a,
  title = {Trend Analysis of {{COVID-19}} Mis/Disinformation Narratives--{{A}} 3-Year Study},
  author = {Kotseva, Bonka and Vianini, Irene and Nikolaidis, Nikolaos and Faggiani, Nicol{\`o} and Potapova, Kristina and Gasparro, Caroline and Steiner, Yaniv and Scornavacche, Jessica and Jacquet, Guillaume and Dragu, Vlad and della Rocca, Leonida and Bucci, Stefano and Podavini, Aldo and Verile, Marco and Macmillan, Charles and Linge, Jens P.},
  year = 2023,
  month = nov,
  journal = {PLOS ONE},
  volume = {18},
  number = {11},
  pages = {e0291423},
  publisher = {Public Library of Science},
  issn = {1932-6203},
  doi = {10.1371/journal.pone.0291423},
  urldate = {2024-09-30},
  abstract = {To tackle the COVID-19 infodemic, we analysed 58,625 articles from 460 unverified sources, that is, sources that were indicated by fact checkers and other mis/disinformation experts as frequently spreading mis/disinformation, covering the period from 1 January 2020 to 31 December 2022. Our aim was to identify the main narratives of COVID-19 mis/disinformation, develop a codebook, automate the process of narrative classification by training an automatic classifier, and analyse the spread of narratives over time and across countries. Articles were retrieved with a customised version of the Europe Media Monitor (EMM) processing chain providing a stream of text items. Machine translation was employed to automatically translate non-English text to English and clustering was carried out to group similar articles. A multi-level codebook of COVID-19 mis/disinformation narratives was developed following an inductive approach; a transformer-based model was developed to classify all text items according to the codebook. Using the transformer-based model, we identified 12 supernarratives that evolved over the three years studied. The analysis shows that there are often real events behind mis/disinformation trends, which unverified sources misrepresent or take out of context. We established a process that allows for near real-time monitoring of COVID-19 mis/disinformation. This experience will be useful to analyse mis/disinformation about other topics, such as climate change, migration, and geopolitical developments.},
  langid = {english},
  keywords = {COVID 19,European Union,Pandemics,United States,Vaccination and immunization,Vaccine development,Vaccines,Viral vaccines},
  annotation = {Read\_Status: To Read\\
Read\_Status\_Date: 2025-07-09T10:12:35.494Z},
  file = {C:\Users\nasty\Documents\Zotero\Kotseva et al. - 2023 - Trend analysis of COVID-19 misdisinformation narratives–A 3-year study 2.pdf}
}

@article{kullbackInformationSufficiency1951,
  title = {On {{Information}} and {{Sufficiency}}},
  author = {Kullback, Solomon and Leibler, Richard A.},
  year = {1951},
  month = mar,
  journal = {The Annals of Mathematical Statistics},
  volume = {22},
  number = {1},
  pages = {79--86},
  issn = {0003-4851},
  doi = {10.1214/aoms/1177729694},
  urldate = {2024-12-10},
  abstract = {Semantic Scholar extracted view of "On Information and Sufficiency" by S. Kullback et al.},
  langid = {english},
  file = {C\:\\Users\\nasty\\Zotero\\storage\\4P8HMMYI\\Kullback and Leibler - 1951 - On Information and Sufficiency.pdf;C\:\\Users\\nasty\\Zotero\\storage\\8TZNN3JG\\Kullback and Leibler - 1951 - On Information and Sufficiency.pdf;C\:\\Users\\nasty\\Zotero\\storage\\IIPJSC23\\document.html}
}

@inproceedings{kusnerWordEmbeddingsDocument2015,
  title = {From {{Word Embeddings To Document Distances}}},
  booktitle = {Proceedings of the 32nd {{International Conference}} on {{Machine Learning}}},
  author = {Kusner, Matt and Sun, Yu and Kolkin, Nicholas and Weinberger, Kilian},
  year = 2015,
  month = jun,
  pages = {957--966},
  publisher = {PMLR},
  issn = {1938-7228},
  urldate = {2024-12-05},
  abstract = {We present the Word Mover's Distance (WMD), a novel distance function between text documents. Our work is based on recent results in word embeddings that learn semantically meaningful representations for words from local co-occurrences in sentences. The WMD distance measures the dissimilarity between two text documents as the minimum amount of distance that the embedded words of one document need to "travel" to reach the embedded words of another document. We show that this distance metric can be cast as an instance of the Earth Mover's Distance, a well studied transportation problem for which several highly efficient solvers have been developed. Our metric has no hyperparameters and is straight-forward to implement. Further, we demonstrate on eight real world document classification data sets, in comparison with seven state-of-the-art baselines, that the WMD metric leads to unprecedented low k-nearest neighbor document classification error rates.},
  langid = {english},
  annotation = {Read\_Status: To Read\\
Read\_Status\_Date: 2025-07-28T14:26:16.223Z},
  file = {C:\Users\nasty\Documents\Zotero\Kusner et al. - 2015 - From Word Embeddings To Document Distances 1.pdf}
}

@inproceedings{leeLFTKHandcraftedFeatures2023,
  title = {{{LFTK}}: {{Handcrafted Features}} in {{Computational Linguistics}}},
  shorttitle = {{{LFTK}}},
  booktitle = {Proceedings of the 18th {{Workshop}} on {{Innovative Use}} of {{NLP}} for {{Building Educational Applications}} ({{BEA}} 2023)},
  author = {Lee, Bruce W. and Lee, Jason},
  editor = {Kochmar, Ekaterina and Burstein, Jill and Horbach, Andrea and {Laarmann-Quante}, Ronja and Madnani, Nitin and Tack, Ana{\"i}s and Yaneva, Victoria and Yuan, Zheng and Zesch, Torsten},
  year = 2023,
  month = jul,
  pages = {1--19},
  publisher = {Association for Computational Linguistics},
  address = {Toronto, Canada},
  doi = {10.18653/v1/2023.bea-1.1},
  urldate = {2024-10-18},
  abstract = {Past research has identified a rich set of handcrafted linguistic features that can potentially assist various tasks. However, their extensive number makes it difficult to effectively select and utilize existing handcrafted features. Coupled with the problem of inconsistent implementation across research works, there has been no categorization scheme or generally-accepted feature names. This creates unwanted confusion. Also, no actively-maintained open-source library extracts a wide variety of handcrafted features. The current handcrafted feature extraction practices have several inefficiencies, and a researcher often has to build such an extraction system from the ground up. We collect and categorize more than 220 popular handcrafted features grounded on past literature. Then, we conduct a correlation analysis study on several task-specific datasets and report the potential use cases of each feature. Lastly, we devise a multilingual handcrafted linguistic feature extraction system in a systematically expandable manner. We open-source our system to give the community a rich set of pre-implemented handcrafted features.},
  annotation = {Read\_Status: Not Reading\\
Read\_Status\_Date: 2025-07-28T14:29:13.792Z},
  file = {C\:\\Users\\nasty\\Documents\\Zotero\\Lee and Lee - 2023 - LFTK Handcrafted Features in Computational Linguistics 2.pdf;C\:\\Users\\User\\OneDrive - Universität des Saarlandes\\Documents\\Zotero\\Lee and Lee - 2023 - LFTK Handcrafted Features in Computational Linguistics.pdf}
}

@inproceedings{leePhishGANDataAugmentation2020,
  title = {{{PhishGAN}}: {{Data Augmentation}} and {{Identification}} of {{Homoglpyh Attacks}}},
  shorttitle = {{{PhishGAN}}},
  booktitle = {2020 {{International Conference}} on {{Communications}}, {{Computing}}, {{Cybersecurity}}, and {{Informatics}} ({{CCCI}})},
  author = {Lee, Joon Sern and Yam, Gui Peng David and Chan, Jin Hao},
  year = 2020,
  month = nov,
  eprint = {2006.13742},
  primaryclass = {cs},
  pages = {1--6},
  doi = {10.1109/CCCI49893.2020.9256804},
  urldate = {2025-03-20},
  abstract = {Homoglyph attacks are a common technique used by hackers to conduct phishing. Domain names or links that are visually similar to actual ones are created via punycode to obfuscate the attack, making the victim more susceptible to phishing. For example, victims may mistake "\textbar inkedin.com" for "linkedin.com" and in the process, divulge personal details to the fake website. Current State of The Art (SOTA) typically make use of string comparison algorithms (e.g. Levenshtein Distance), which are computationally heavy. One reason for this is the lack of publicly available datasets thus hindering the training of more advanced Machine Learning (ML) models. Furthermore, no one font is able to render all types of punycode correctly, posing a significant challenge to the creation of a dataset that is unbiased toward any particular font. This coupled with the vast number of internet domains pose a challenge in creating a dataset that can capture all possible variations. Here, we show how a conditional Generative Adversarial Network (GAN), PhishGAN, can be used to generate images of hieroglyphs, conditioned on non-homoglpyh input text images. Practical changes to current SOTA were required to facilitate the generation of more varied homoglyph text-based images. We also demonstrate a workflow of how PhishGAN together with a Homoglyph Identifier (HI) model can be used to identify the domain the homoglyph was trying to imitate. Furthermore, we demonstrate how PhishGAN's ability to generate datasets on the fly facilitate the quick adaptation of cybersecurity systems to detect new threats as they emerge.},
  archiveprefix = {arXiv},
  keywords = {Computer Science - Computer Vision and Pattern Recognition,Computer Science - Cryptography and Security,Computer Science - Machine Learning},
  annotation = {Read\_Status: Not Reading\\
Read\_Status\_Date: 2025-07-28T14:29:22.047Z},
  file = {C\:\\Users\\nasty\\Documents\\Zotero\\Lee et al. - 2020 - PhishGAN Data Augmentation and Identification of Homoglpyh Attacks.pdf;C\:\\Users\\nasty\\Zotero\\storage\\IDWG4EMB\\2006.html}
}

@article{liefkeNaturalLanguageOntology2024,
  title = {Natural {{Language Ontology}} and {{Semantic Theory}}},
  author = {Liefke, Kristina},
  year = 2024,
  month = dec,
  journal = {Elements in Semantics},
  publisher = {Cambridge University Press},
  doi = {10.1017/9781009307789},
  urldate = {2024-12-20},
  abstract = {Cambridge Core - Semantics and Pragmatics - Natural Language Ontology and Semantic Theory},
  isbn = {9781009307789 9781009539272 9781009307802},
  langid = {english},
  annotation = {Read\_Status: To Read\\
Read\_Status\_Date: 2025-08-21T12:40:26.177Z},
  file = {C:\Users\nasty\Documents\Zotero\Liefke - 2024 - Natural Language Ontology and Semantic Theory.pdf}
}

@book{liefkeReductionUnificationNatural2025,
  title = {Reduction and {{Unification}} in {{Natural Language Ontology}}},
  author = {Liefke, Kristina},
  year = 2025,
  month = jan,
  edition = {1},
  publisher = {Cambridge University Press},
  doi = {10.1017/9781009559683},
  urldate = {2024-12-20},
  abstract = {Semantic theories for natural language assume many different kinds of objects, including (among many others) individuals, properties, events, degrees, and kinds. Formal type-theoretic semantics tames this 'zoo' of objects by assuming only a small number of ontologically primitive categories and by obtaining the objects of all other categories through constructions out of these primitives. This Element surveys arguments for this reduction of semantic categories. It compares the ontological commitments of different such reductions and establishes relations between competing foundational semantic ontologies. In doing so, it yields insights into the requirements on minimal semantic ontologies for natural language and the challenges for semantic ontology engineering.},
  copyright = {https://www.cambridge.org/core/terms},
  isbn = {978-1-009-55968-3 978-1-009-55964-5 978-1-009-55965-2},
  langid = {english},
  annotation = {Read\_Status: To Read\\
Read\_Status\_Date: 2025-07-28T14:30:12.336Z},
  file = {C:\Users\nasty\Documents\Zotero\Liefke - 2025 - Reduction and Unification in Natural Language Ontology.pdf}
}

@article{lima-lopesLexicalPatternsReligious2025,
  title = {Lexical {{Patterns}} of {{Religious Conservatism}}: {{A Study}} of {{Social Media Reactions}} to an {{Art Exhibition}} in {{Brazil}}},
  shorttitle = {Lexical {{Patterns}} of {{Religious Conservatism}}},
  author = {de {Lima-Lopes}, Rodrigo Esteves},
  year = 2025,
  month = mar,
  journal = {Digital Studies / Le champ num\'erique},
  volume = {15},
  number = {1},
  publisher = {Open Library of Humanities},
  issn = {1918-3666},
  doi = {10.16995/dscn.15132},
  urldate = {2025-03-06},
  abstract = {This study examines the dynamics of hate speech on social media, focusing on comments opposing the announcement of the Queermuseu -- Cartografias da Diferen\c ca na Arte Brasileira (Cartographies of Diversity in Brazilian Art) exhibition in southern Brazil. Employing appraisal system, Systemic-Functional Linguistics, and network analysis, the research investigates the linguistic and semantic patterns underlying the interactions. Lexical and interaction networks were qualitatively interpreted in order to understand the interaction amongst users and the reaction towards such exhibition. Results reveal that conservative discourse significantly drives comments towards negative evaluation, framing the exhibition as a threat to religious and traditional family values. This research contributes to understanding the role of digital platforms in amplifying intolerance and the mechanisms of polarized discourse in Brazilian Portuguese. Cette \'etude examine la dynamique des discours de haine sur les r\'eseaux sociaux, en se concentrant sur les commentaires oppos\'es \`a l'annonce de l'exposition Queermuseu -- Cartografias da Diferen\c ca na Arte Brasileira (Cartographies de la Diversit\'e dans l'Art Br\'esilien) dans le sud du Br\'esil. En utilisant le syst\`eme d'appr\'eciation, la Linguistique Syst\'emique-Fonctionnelle et l'analyse des r\'eseaux, la recherche explore les sch\'emas linguistiques et s\'emantiques sous-jacents aux interactions. Les r\'eseaux lexicaux et interactionnels ont \'et\'e interpr\'et\'es qualitativement pour comprendre les interactions entre les utilisateurs et les r\'eactions face \`a cette exposition. Les r\'esultats montrent que le discours conservateur alimente largement les commentaires \`a travers une \'evaluation n\'egative, pr\'esentant l'exposition comme une menace pour les valeurs religieuses et familiales traditionnelles. Cette recherche contribue \`a la compr\'ehension du r\^ole des plateformes num\'eriques dans l'amplification de l'intol\'erance et des m\'ecanismes de discours polaris\'es en portugais br\'esilien.},
  copyright = {Copyright: \copyright{} 2025 The Author(s). This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. See http://creativecommons.org/licenses/by/4.0/.},
  langid = {english},
  annotation = {Read\_Status: Not Reading\\
Read\_Status\_Date: 2025-07-28T14:33:54.250Z},
  file = {C:\Users\nasty\Documents\Zotero\Lima-Lopes - 2025 - Lexical Patterns of Religious Conservatism A Study of Social Media Reactions to an Art Exhibition i.pdf}
}

@article{mahowaldInfoInformationTheory2013,
  title = {Info/Information Theory: {{Speakers}} Choose Shorter Words in Predictive Contexts},
  shorttitle = {Info/Information Theory},
  author = {Mahowald, Kyle and Fedorenko, Evelina and Piantadosi, Steven T. and Gibson, Edward},
  year = 2013,
  month = feb,
  journal = {Cognition},
  volume = {126},
  number = {2},
  pages = {313--318},
  issn = {00100277},
  doi = {10.1016/j.cognition.2012.09.010},
  urldate = {2024-10-01},
  abstract = {A major open question in natural language research is the role of communicative efficiency in the origin and on-line processing of language structures. Here, we use word pairs like chimp/chimpanzee, which differ in length but have nearly identical meanings, to investigate the communicative properties of lexical systems and the communicative pressures on language users. If language is designed to be information-theoretically optimal, then shorter words should convey less information than their longer counterparts, when controlling for meaning. Consistent with this prediction, a corpus analysis revealed that the short form of our meaning-matched pairs occurs in more predictive contexts than the longer form. Second, a behavioral study showed that language users choose the short form more often in predictive contexts, suggesting that tendencies to be information-theoretically efficient manifest in explicit behavioral choices. Our findings, which demonstrate the prominent role of communicative efficiency in the structure of the lexicon, complement and extend the results of Piantadosi, Tily, and Gibson (2011), who showed that word length is better correlated with Shannon information content than with frequency. Crucially, we show that this effect arises at least in part from active speaker choice.},
  copyright = {https://www.elsevier.com/tdm/userlicense/1.0/},
  langid = {english},
  keywords = {Communication,Efficiency,Information theory,Language,Psycholinguistics},
  annotation = {Read\_Status: Read\\
Read\_Status\_Date: 2025-07-28T14:34:26.457Z},
  file = {C\:\\Users\\nasty\\Documents\\Zotero\\Mahowald et al. - 2013 - Infoinformation theory Speakers choose shorter words in predictive contexts 1.pdf;C\:\\Users\\nasty\\Documents\\Zotero\\Mahowald et al. - 2013 - Infoinformation theory Speakers choose shorter words in predictive contexts 2.pdf;C\:\\Users\\nasty\\Documents\\Zotero\\Mahowald et al. - 2013 - Infoinformation theory Speakers choose shorter words in predictive contexts 3.pdf;C\:\\Users\\User\\OneDrive - Universität des Saarlandes\\Documents\\Zotero\\Mahowald et al. - 2013 - Infoinformation theory Speakers choose shorter words in predictive contexts.pdf;C\:\\Users\\nasty\\Zotero\\storage\\239J7P9I\\S0010027712002107.html;C\:\\Users\\nasty\\Zotero\\storage\\ADDTSN6P\\S0010027712002107.html;C\:\\Users\\nasty\\Zotero\\storage\\S2AZ6GEJ\\S0010027712002107.html}
}

@inproceedings{marjanenClusteringIdeologicalTerms2019,
  title = {Clustering {{Ideological Terms}} in {{Historical Newspaper Data}} with {{Diachronic Word Embeddings}}},
  booktitle = {{{HistoInformatics}}@{{TPDL}}},
  author = {Marjanen, Jani and Pivovarova, Lidia and Zosa, Elaine and Kurunm{\"a}ki, Jussi},
  year = 2019,
  urldate = {2024-12-16},
  abstract = {During the course of the nineteenth century, ideological language mostly expressed through isms such as liberalism, socialism or conservatism, entered the lexicon in most European languages. Previous research has based on reading key texts claimed that the suffix ism was introduced to new linguistic domains during the period up to WWI, many of which do not relate to ideology. This paper uses a data-driven way to study the emergence of isms in nineteenth-century Finnish newspapers and uses word embeddings to cluster them and to trace their thematic expansion in the period. As such, the study provides a quantitatively sound way of tracking how isms relate to ideological language and more generally contributes to the understanding of the development of political language in Finland.},
  annotation = {Read\_Status: To Read\\
Read\_Status\_Date: 2025-07-28T14:37:32.456Z},
  file = {C:\Users\nasty\Documents\Zotero\Marjanen et al. - 2019 - Clustering Ideological Terms in Historical Newspaper Data with Diachronic Word Embeddings 1.pdf}
}

@book{martinFactualWritingExploring1989,
  title = {Factual {{Writing}}: {{Exploring}} and {{Challenging Social Reality}}},
  shorttitle = {Factual {{Writing}}},
  author = {Martin, J. R.},
  year = 1989,
  publisher = {Oxford University Press},
  abstract = {This book describes the different types of writing that are used as tools of communication in the adult world, and compares the writing tasks that teachers set for their pupils and the ways in which they measure success. By analyzing the different skills required within the school context and the outside world, Martin suggests how the education process could become more appropriate to the needs of the individual.},
  googlebooks = {xLE0AAAAMAAJ},
  isbn = {978-0-19-437158-2},
  langid = {english},
  keywords = {Language Arts & Disciplines / Rhetoric,narrative_theory},
  annotation = {Read\_Status: To Read\\
Read\_Status\_Date: 2025-08-20T13:19:55.263Z}
}

@misc{meguellatiDetectingPersuasionSocial2025,
  title = {Towards {{Detecting Persuasion}} on {{Social Media}}: {{From Model Development}} to {{Insights}} on {{Persuasion Strategies}}},
  shorttitle = {Towards {{Detecting Persuasion}} on {{Social Media}}},
  author = {Meguellati, Elyas and Civelli, Stefano and Bernardelle, Pietro and Sadiq, Shazia and King, Irwin and Demartini, Gianluca},
  year = 2025,
  month = jul,
  number = {arXiv:2503.13844},
  eprint = {2503.13844},
  primaryclass = {cs},
  publisher = {arXiv},
  doi = {10.48550/arXiv.2503.13844},
  urldate = {2025-08-05},
  abstract = {Political advertising plays a pivotal role in shaping public opinion and influencing electoral outcomes, often through subtle persuasive techniques embedded in broader propaganda strategies. Detecting these persuasive elements is crucial for enhancing voter awareness and ensuring transparency in democratic processes. This paper presents an integrated approach that bridges model development and real-world application through two interconnected studies. First, we introduce a lightweight model for persuasive text detection that achieves state-of-the-art performance in Subtask 3 of SemEval 2023 Task 3 while requiring significantly fewer computational resources and training data than existing methods. Second, we demonstrate the model's practical utility by collecting the Australian Federal Election 2022 Facebook Ads (APA22) dataset, partially annotating a subset for persuasion, and fine-tuning the model to adapt from mainstream news to social media content. We then apply the fine-tuned model to label the remainder of the APA22 dataset, revealing distinct patterns in how political campaigns leverage persuasion through different funding strategies, word choices, demographic targeting, and temporal shifts in persuasion intensity as election day approaches. Our findings not only underscore the necessity of domain-specific modeling for analyzing persuasion on social media but also show how uncovering these strategies can enhance transparency, inform voters, and promote accountability in digital campaigns.},
  archiveprefix = {arXiv},
  keywords = {Computer Science - Artificial Intelligence,Computer Science - Computation and Language,Computer Science - Computers and Society,Computer Science - Machine Learning,icwsm2025},
  annotation = {Read\_Status: To Read\\
Read\_Status\_Date: 2025-08-20T13:20:07.706Z},
  file = {C\:\\Users\\nasty\\Documents\\Zotero\\Meguellati et al. - 2025 - Towards Detecting Persuasion on Social Media From Model Development to Insights on Persuasion Strat 1.pdf;C\:\\Users\\nasty\\Documents\\Zotero\\Meguellati et al. - 2025 - Towards Detecting Persuasion on Social Media From Model Development to Insights on Persuasion Strat.pdf;C\:\\Users\\nasty\\Zotero\\storage\\VGWWZ6CD\\2503.html}
}

@inproceedings{meguellatiDualityPersuasionPersonalization2025,
  title = {The {{Duality}} of {{Persuasion}}: {{Between Personalization}} and {{Detection}}},
  shorttitle = {The {{Duality}} of {{Persuasion}}},
  booktitle = {Companion {{Proceedings}} of the {{ACM}} on {{Web Conference}} 2025},
  author = {Meguellati, Elyas},
  year = 2025,
  month = may,
  series = {{{WWW}} '25},
  pages = {705--708},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  doi = {10.1145/3701716.3715276},
  urldate = {2025-07-01},
  abstract = {This PhD project investigates the duality of artificial intelligence in persuasive communication, exploring both the generation of personalized content and the detection of persuasive strategies. Insights are drawn from four studies. The first demonstrates how large language models (LLMs) can generate personalized online advertisements targeting specific personality traits, such as openness and neuroticism, with effectiveness comparable to human-written ads. The second introduces a lightweight transformer model for persuasive text detection, achieving state-of-the-art performance in Subtask 3 of SemEval 2023 and applying it to Facebook political ads from the 2022 Australian Federal election campaign. The third, ongoing study extends to persuasive memes detection by employing LLMs for preprocessing and data augmentation before model analysis. Finally, a planned study will explore a dynamic personalization: Developing adaptive LLM frameworks to tailor personalized messages over time based on user feedback or interactions. These studies collectively contribute to the understanding and ethical application of AI in persuasive communication, offering insights for academia, industry, and policymakers while addressing societal implications of AI-driven persuasion.},
  isbn = {979-8-4007-1331-6},
  keywords = {icwsm2025},
  annotation = {Read\_Status: To Read\\
Read\_Status\_Date: 2025-07-03T15:00:57.711Z},
  file = {C:\Users\nasty\Documents\Zotero\Meguellati - 2025 - The Duality of Persuasion Between Personalization and Detection.pdf}
}

@inproceedings{meguellatiLLMBasedSemanticAugmentation2025,
  title = {{{LLM-Based Semantic Augmentation}} for {{Harmful Content Detection}}},
  booktitle = {Proceedings of the {{International AAAI Conference}} on {{Web}} and {{Social Media}}},
  author = {Meguellati, Elyas and Zeghina, Assaad and Sadiq, Shazia and Demartini, Gianluca},
  year = 2025,
  volume = {19},
  pages = {1190--1209},
  urldate = {2025-07-01},
  keywords = {icwsm2025},
  annotation = {Read\_Status: To Read\\
Read\_Status\_Date: 2025-07-03T15:07:34.532Z},
  file = {C:\Users\nasty\Documents\Zotero\Meguellati et al. - 2025 - LLM-Based Semantic Augmentation for Harmful Content Detection.pdf}
}

@article{mejovaNarrativesWarUkrainian2025,
  title = {Narratives of {{War}}: {{Ukrainian Memetic Warfare}} on {{Twitter}}},
  shorttitle = {Narratives of {{War}}},
  author = {Mejova, Yelena and Capozzi, Arthur and Monti, Corrado and Morales, Gianmarco De Francisci},
  year = 2025,
  month = may,
  journal = {Proceedings of the ACM on Human-Computer Interaction},
  volume = {9},
  number = {2},
  eprint = {2309.08363},
  primaryclass = {cs},
  pages = {1--28},
  issn = {2573-0142},
  doi = {10.1145/3711037},
  urldate = {2025-09-18},
  abstract = {The 2022 Russian invasion of Ukraine has seen an intensification in the use of social media by governmental actors in cyber warfare. Wartime communication via memes has been a successful strategy used not only by independent accounts such as @uamemesforces, but also-for the first time in a full-scale interstate war-by official Ukrainian government accounts such as @Ukraine and @DefenceU. We study this prominent example of memetic warfare through the lens of its narratives, and find them to be a key component of success: tweets with a 'victim' narrative garner twice as many retweets. However, malevolent narratives focusing on the enemy resonate more than those about heroism or victims with countries providing more assistance to Ukraine. Our findings present a nuanced examination of Ukraine's influence operations and of the worldwide response to it, thus contributing new insights into the evolution of socio-technical systems in times of war.},
  archiveprefix = {arXiv},
  keywords = {Computer Science - Computers and Society,Computer Science - Human-Computer Interaction,Computer Science - Social and Information Networks},
  annotation = {Read\_Status: To Read\\
Read\_Status\_Date: 2025-09-25T18:25:45.635Z},
  file = {C\:\\Users\\nasty\\Documents\\Zotero\\Mejova et al. - 2025 - Narratives of War Ukrainian Memetic Warfare on Twitter.pdf;C\:\\Users\\nasty\\Zotero\\storage\\P7TJPNGH\\2309.html}
}

@article{mensPositioningPoliticalTexts2025,
  title = {Positioning {{Political Texts}} with {{Large Language Models}} by {{Asking}} and {{Averaging}}},
  author = {Mens, Ga{\"e}l Le and Gallego, Aina},
  year = 2025,
  month = jul,
  journal = {Political Analysis},
  volume = {33},
  number = {3},
  pages = {274--282},
  issn = {1047-1987, 1476-4989},
  doi = {10.1017/pan.2024.29},
  urldate = {2025-08-08},
  abstract = {We use instruction-tuned large language models (LLMs) like GPT-4, Llama 3, MiXtral, or Aya to position political texts within policy and ideological spaces. We ask an LLM where a tweet or a sentence of a political text stands on the focal dimension and take the average of the LLM responses to position political actors such as US Senators, or longer texts such as UK party manifestos or EU policy speeches given in 10 different languages. The correlations between the position estimates obtained with the best LLMs and benchmarks based on text coding by experts, crowdworkers, or roll call votes exceed .90. This approach is generally more accurate than the positions obtained with supervised classifiers trained on large amounts of research data. Using instruction-tuned LLMs to position texts in policy and ideological spaces is fast, cost-efficient, reliable, and reproducible (in the case of open LLMs) even if the texts are short and written in different languages. We conclude with cautionary notes about the need for empirical validation.},
  langid = {english},
  keywords = {camp3_helsinki,ideology,LLM,scaling,text as data},
  annotation = {Read\_Status: Not Reading\\
Read\_Status\_Date: 2025-08-25T14:41:17.049Z},
  file = {C:\Users\nasty\Documents\Zotero\Mens and Gallego - 2025 - Positioning Political Texts with Large Language Models by Asking and Averaging.pdf}
}

@article{mianiLooseTightCreative2024,
  title = {Loose and {{Tight}}: {{Creative Formation}} but {{Rigid Use}} of {{Nominal Compounds}} in {{Conspiracist Texts}}},
  shorttitle = {Loose and {{Tight}}},
  author = {Miani, Alessandro and {van der Plas}, Lonneke and Bangerter, Adrian},
  year = 2024,
  journal = {The Journal of Creative Behavior},
  volume = {58},
  number = {1},
  pages = {114--127},
  issn = {2162-6057},
  doi = {10.1002/jocb.633},
  urldate = {2024-09-30},
  abstract = {Conspiracy theories (CTs) are spectacular narratives, widely spread, that pose societal threats. We test whether CTs might be linguistically creative products, which would facilitate their transmission and thereby account for their widespread popularity. We analyzed nominal compounds (e.g., mind control, carbon dioxide; N = 1,713,568) from a large corpus of conspiracist and mainstream texts matched by topic. In conspiracist texts, compounds showed greater originality, divergence, and sophistication, but they were used with lower frequency and were more often repeated in different contexts. This pattern suggests a creative aspect in the generation of compounds, coupled with rigidity in their use. We interpret these findings as an effect of loosely defined conceptual boundaries among conspiracist writers, in conjunction with social functions like status or group signaling. Our findings not only contribute to the discourse on creativity in CTs but also provide insights into the communicative advantage of CTs.},
  langid = {english},
  keywords = {conspiracy theories,creativity,natural language processing,nominal compounds},
  annotation = {Read\_Status: To Read\\
Read\_Status\_Date: 2025-07-28T14:39:39.221Z},
  file = {C\:\\Users\\nasty\\Documents\\Zotero\\Miani et al. - 2024 - Loose and Tight Creative Formation but Rigid Use of Nominal Compounds in Conspiracist Texts 2.pdf;C\:\\Users\\User\\OneDrive - Universität des Saarlandes\\Documents\\Zotero\\Miani et al. - 2024 - Loose and Tight Creative Formation but Rigid Use of Nominal Compounds in Conspiracist Texts.pdf;C\:\\Users\\nasty\\Zotero\\storage\\H5367IJU\\jocb.html;C\:\\Users\\nasty\\Zotero\\storage\\X9RD6XPI\\jocb.html}
}

@inproceedings{mikolovEfficientEstimationWord2013d,
  title = {Efficient {{Estimation}} of {{Word Representations}} in {{Vector Space}}},
  booktitle = {1st {{International Conference}} on {{Learning Representations}}, {{ICLR}} 2013, {{Scottsdale}}, {{Arizona}}, {{USA}}, {{May}} 2-4, 2013, {{Workshop Track Proceedings}}},
  author = {Mikolov, Tom{\'a}s and Chen, Kai and Corrado, Greg and Dean, Jeffrey},
  editor = {Bengio, Yoshua and LeCun, Yann},
  year = {2013},
  urldate = {2025-03-11}
}

@article{mohammadinodooshanSuccessfulRhetoricsHow2025,
  title = {Successful {{Rhetorics}}: {{How Do Linguistic Dimensions Affect User Engagement}} with {{Different News Categories}} on {{Twitter}}?},
  shorttitle = {Successful {{Rhetorics}}},
  author = {Mohammadinodooshan, Alireza and Carlsson, Niklas},
  year = 2025,
  month = jun,
  journal = {Proceedings of the International AAAI Conference on Web and Social Media},
  volume = {19},
  pages = {1246--1261},
  issn = {2334-0770},
  doi = {10.1609/icwsm.v19i1.35871},
  urldate = {2025-07-01},
  abstract = {This paper analyzes how different rhetorical attributes in news tweets, specifically analytical, clout, perceptual, and risk language, influence user engagement across publishers with different bias and reliability ratings. Using the LIWC framework to quantify these linguistic dimensions in a 5.5 million tweets dataset covering 1,553 news publishers and capturing over 480 million tweet interactions, we perform and present a category-based analysis that captures the relative impact that such features have on the user engagement rates associated with different political bias and reliability categories. While highly biased and unreliable publishers saw increased engagement for clout and risk language, confirming audience biases, the least biased ones benefited more from analytical language. Perception language, on the other hand, uniformly reduced engagement. These insights not only further our understanding of persuasion tactics but also have implications for curbing misinformation by aligning recommendations with audience veracity and impartiality preferences.},
  copyright = {Copyright (c) 2025 Association for the Advancement of Artificial Intelligence},
  langid = {english},
  keywords = {icwsm2025},
  annotation = {Read\_Status: To Read\\
Read\_Status\_Date: 2025-07-03T15:08:30.254Z},
  file = {C:\Users\nasty\Documents\Zotero\Mohammadinodooshan and Carlsson - 2025 - Successful Rhetorics How Do Linguistic Dimensions Affect User Engagement with Different News Catego.pdf}
}

@inproceedings{montariolScalableInterpretableSemantic2021a,
  title = {Scalable and {{Interpretable Semantic Change Detection}}},
  booktitle = {Proceedings of the 2021 {{Conference}} of the {{North American Chapter}} of the {{Association}} for {{Computational Linguistics}}: {{Human Language Technologies}}},
  author = {Montariol, Syrielle and Martinc, Matej and Pivovarova, Lidia},
  editor = {Toutanova, Kristina and Rumshisky, Anna and Zettlemoyer, Luke and {Hakkani-Tur}, Dilek and Beltagy, Iz and Bethard, Steven and Cotterell, Ryan and Chakraborty, Tanmoy and Zhou, Yichao},
  year = 2021,
  month = jun,
  pages = {4642--4652},
  publisher = {Association for Computational Linguistics},
  address = {Online},
  doi = {10.18653/v1/2021.naacl-main.369},
  urldate = {2024-11-07},
  abstract = {Several cluster-based methods for semantic change detection with contextual embeddings emerged recently. They allow a fine-grained analysis of word use change by aggregating embeddings into clusters that reflect the different usages of the word. However, these methods are unscalable in terms of memory consumption and computation time. Therefore, they require a limited set of target words to be picked in advance. This drastically limits the usability of these methods in open exploratory tasks, where each word from the vocabulary can be considered as a potential target. We propose a novel scalable method for word usage-change detection that offers large gains in processing time and significant memory savings while offering the same interpretability and better performance than unscalable methods. We demonstrate the applicability of the proposed method by analysing a large corpus of news articles about COVID-19.},
  annotation = {Read\_Status: To Read\\
Read\_Status\_Date: 2025-07-28T14:44:59.444Z},
  file = {C:\Users\nasty\Documents\Zotero\Montariol et al. - 2021 - Scalable and Interpretable Semantic Change Detection 2.pdf}
}

@article{moralOverviewDIPROMATS20242024,
  title = {{Overview of DIPROMATS 2024: Detection, Characterization and Tracking of Propaganda in Messages from Diplomats and Authorities of World Powers}},
  shorttitle = {{Overview of DIPROMATS 2024}},
  author = {Moral, Pablo and Fraile, Jes{\'u}s M. and Marco, Guillermo and Pe{\~n}as, Anselmo and Gonzalo, Julio},
  year = 2024,
  month = sep,
  journal = {Procesamiento del Lenguaje Natural},
  volume = {73},
  number = {0},
  pages = {347--358},
  issn = {1989-7553},
  urldate = {2025-06-23},
  abstract = {This paper summarizes the findings of DIPROMATS 2024, a challenge included at the Iberian Languages Evaluation Forum (IberLEF). This second edition introduces a refined typology of techniques and a more balanced dataset for propaganda detection, alongside a new task focused on identifying strategic narratives. The dataset for the first task includes 12,012 annotated tweets in English and 9,501 in Spanish, posted by authorities from China, Russia, the United States, and the European Union. Participants tackled three subtasks in each language: binary classification to detect propagandistic tweets, clustering tweets into three propaganda categories, and fine-grained categorization using seven techniques. The second task presents a multi-class, multi-label classification challenge where systems identify which predefined narratives (associated with each international actor) tweets belong to. This task is supported by narrative descriptions and example tweets in English and Spanish, using few-shot learning techniques. 40 runs from nine different teams were evaluated.},
  copyright = {Copyright (c) 2024 Procesamiento del Lenguaje Natural},
  langid = {spanish},
  annotation = {Read\_Status: To Read\\
Read\_Status\_Date: 2025-07-07T14:19:48.811Z},
  file = {C:\Users\nasty\Documents\Zotero\Moral et al. - 2024 - Overview of DIPROMATS 2024 Detection, Characterization and Tracking of Propaganda in Messages from.pdf}
}

@inproceedings{mosbachCloserLookLinguistic2020,
  title = {A {{Closer Look}} at {{Linguistic Knowledge}} in {{Masked Language Models}}: {{The Case}} of {{Relative Clauses}} in {{American English}}},
  shorttitle = {A {{Closer Look}} at {{Linguistic Knowledge}} in {{Masked Language Models}}},
  booktitle = {Proceedings of the 28th {{International Conference}} on {{Computational Linguistics}}},
  author = {Mosbach, Marius and {Degaetano-Ortlieb}, Stefania and Krielke, Marie-Pauline and Abdullah, Badr M. and Klakow, Dietrich},
  editor = {Scott, Donia and Bel, Nuria and Zong, Chengqing},
  year = 2020,
  month = dec,
  pages = {771--787},
  publisher = {International Committee on Computational Linguistics},
  address = {Barcelona, Spain (Online)},
  doi = {10.18653/v1/2020.coling-main.67},
  urldate = {2024-10-01},
  abstract = {Transformer-based language models achieve high performance on various tasks, but we still lack understanding of the kind of linguistic knowledge they learn and rely on. We evaluate three models (BERT, RoBERTa, and ALBERT), testing their grammatical and semantic knowledge by sentence-level probing, diagnostic cases, and masked prediction tasks. We focus on relative clauses (in American English) as a complex phenomenon needing contextual information and antecedent identification to be resolved. Based on a naturalistic dataset, probing shows that all three models indeed capture linguistic knowledge about grammaticality, achieving high performance. Evaluation on diagnostic cases and masked prediction tasks considering fine-grained linguistic knowledge, however, shows pronounced model-specific weaknesses especially on semantic knowledge, strongly impacting models' performance. Our results highlight the importance of (a)model comparison in evaluation task and (b) building up claims of model performance and the linguistic knowledge they capture beyond purely probing-based evaluations.},
  annotation = {Read\_Status: Not Reading\\
Read\_Status\_Date: 2025-07-28T14:48:00.307Z},
  file = {C\:\\Users\\nasty\\Documents\\Zotero\\Mosbach et al. - 2020 - A Closer Look at Linguistic Knowledge in Masked Language Models The Case of Relative Clauses in Ame 1.pdf;C\:\\Users\\nasty\\Documents\\Zotero\\Mosbach et al. - 2020 - A Closer Look at Linguistic Knowledge in Masked Language Models The Case of Relative Clauses in Ame 1.pdf;C\:\\Users\\nasty\\Documents\\Zotero\\Mosbach et al. - 2020 - A Closer Look at Linguistic Knowledge in Masked Language Models The Case of Relative Clauses in Ame 1.pdf}
}

@article{nationalresearchuniversityhigherschoolofeconomicsDifferentTimesComputer2024,
  title = {{About the past, but at different times: Computer analysis of texts in textbooks on the history of the USSR/Russia for six generations of students}},
  shorttitle = {{About the past, but at different times}},
  author = {{National Research University Higher School of Economics} and Kolmogorova, Anastasia V. and Kolmogorova, Polina A. and {National Research University Higher School of Economics} and Kulikova, Elizaveta R. and {National Research University Higher School of Economics}},
  year = 2024,
  journal = {Vestnik Tomskogo gosudarstvennogo universiteta. Filologiya},
  number = {89},
  pages = {73--103},
  issn = {19986645, 23105046},
  doi = {10.17223/19986645/89/4},
  urldate = {2025-08-25},
  abstract = {In this article, we focus on the analysis of the texts of three history textbooks for university students published at different times: in 1946, 1983, 2001, 2006 and 2010. As a material, we use texts in each of the textbooks describing seven historical topics since the beginnings of the Principality of Kiev till the Reforms of Peter I. In our research, we tried to move away from the tradition banalized in discursive research to analyze history textbooks as a kind of ideologically labeled discourse. Instead, we consider the analyzed texts as a form of manifestation of a certain generational narrative. The authors of the textbooks, being not only institutional narrators, but also representatives of their generation, color the historiographical canvas, which remains, in principle, unchanged, with a certain emotional tone, and, when telling about the same events, shift the focus of thematic attention based on the spirit of their time. To solve this problem, we use computational linguistics methods: sentiment analysis, clusterization and topic modeling. Their use in combination with interpretive analysis allowed us to draw a number of conclusions: (1) there are historical subjects that are told, in general, within the same dominant tonality, while for others there is an ambivalence of evaluation; (2) the ranges even within the same tonality may vary greatly from textbook to textbook; for example, this is characteristic of texts about Ivan the Terrible; (3) each textbook is characterized by its own ``tonal range'': 1946 is the most restrained, while 1983, 2001 and 2006 are the most altitudinal; (4) even in the textbooks of the same author team, published 9 years apart, the tonality of the texts of the same sections is not identical: from 1997 to 2006 it becomes, on the whole, noticeably more positive; (5) within the generational narrative, historical stories are revealed through the prism of a certain dominant idea - it is different for each time: for the post-war narrative it is the idea of protecting the state and paying attention to its geopolitical neighbours; for the post-perestroika period of the formation of the ``young'' Russian democracy (2001) - the idea of paysan community and veche as the forms of original democracy ``of the people''; for the time of the formation of the modern Russian vertical of power (2010) it is the idea of centralization of power and its stability.},
  langid = {russian},
  annotation = {Read\_Status: To Read\\
Read\_Status\_Date: 2025-08-25T16:13:52.430Z},
  file = {C:\Users\nasty\Documents\Zotero\National Research University Higher School of Economics et al. - 2024 - About the past, but at different times Computer analysis of texts in textbooks on the history of th.pdf}
}

@article{nguyenComputationalSociolinguisticsSurvey2016,
  title = {Computational {{Sociolinguistics}}: {{A Survey}}},
  shorttitle = {Computational {{Sociolinguistics}}},
  author = {Nguyen, Dong and Do{\u g}ru{\"o}z, A. Seza and Ros{\'e}, Carolyn P. and {de Jong}, Franciska},
  year = 2016,
  month = sep,
  journal = {Computational Linguistics},
  volume = {42},
  number = {3},
  pages = {537--593},
  publisher = {MIT Press},
  address = {Cambridge, MA},
  doi = {10.1162/COLI_a_00258},
  urldate = {2025-08-27},
  keywords = {example_survey},
  annotation = {Read\_Status: Not Reading\\
Read\_Status\_Date: 2025-09-29T14:35:07.122Z},
  file = {C:\Users\nasty\Documents\Zotero\Nguyen et al. - 2016 - Computational Sociolinguistics A Survey.pdf}
}

@article{oleinikTelegramChannelsCovering2024,
  title = {Telegram Channels Covering {{Russia}}'s Invasion of {{Ukraine}}: A Comparative Analysis of Large Multilingual Corpora},
  shorttitle = {Telegram Channels Covering {{Russia}}'s Invasion of {{Ukraine}}},
  author = {Oleinik, Anton},
  year = 2024,
  month = apr,
  journal = {Journal of Computational Social Science},
  volume = {7},
  number = {1},
  pages = {361--384},
  issn = {2432-2725},
  doi = {10.1007/s42001-023-00240-9},
  urldate = {2025-03-12},
  abstract = {Telegram channels are essential in covering Russia's war in Ukraine. The article compares the war coverage by voenkory, military bloggers in Ukraine and Russia, with war-related news items disseminated by legacy media, both print and electronic (TV, online news portals). A corpus of political and media discourses about the war during its first year was content analyzed using a custom-built Ukrainian-Russian-English-French ``dictionary of war.'' The corpus contains sources from five countries: Ukraine, Russia, the United States, the United Kingdom and France, including the twenty most popular Telegram channels about the war in Ukraine and Russia (119 million words). The study demonstrates the potential of semi-supervised methods of content analysis for studying highly heterogeneous multi-language corpora. It shows how digital propaganda works in the case of war reporting. Military bloggers, actors of white propaganda, appear to outperform bots and throlls. The article also adds a comparative dimension to studies of social media. On the one hand, digital war reporting is compared with war reporting in legacy media. Russian voenkory discussed the invasion in greater technical detail than in national legacy media, whereas Ukrainian voenkory emphasized its ideological dimension more. On the other hand, the comparison extends to include several countries and periods. As the war unfolded, Ukrainian military bloggers' coverage became less ideological and more technical, too.},
  langid = {english},
  keywords = {Content analysis,Dictionary,Military bloggers,Social media,Telegram,War reporting},
  annotation = {Read\_Status: In Progress\\
Read\_Status\_Date: 2025-11-14T16:27:49.017Z},
  file = {C:\Users\nasty\Documents\Zotero\Oleinik - 2024 - Telegram channels covering Russia’s invasion of Ukraine a comparative analysis of large multilingua.pdf}
}

@misc{ostrowskiMultiHopFactChecking2021,
  title = {Multi-{{Hop Fact Checking}} of {{Political Claims}}},
  author = {Ostrowski, Wojciech and Arora, Arnav and Atanasova, Pepa and Augenstein, Isabelle},
  year = 2021,
  month = jun,
  number = {arXiv:2009.06401},
  eprint = {2009.06401},
  publisher = {arXiv},
  doi = {10.48550/arXiv.2009.06401},
  urldate = {2024-11-21},
  abstract = {Recent work has proposed multi-hop models and datasets for studying complex natural language reasoning. One notable task requiring multi-hop reasoning is fact checking, where a set of connected evidence pieces leads to the final verdict of a claim. However, existing datasets either do not provide annotations for gold evidence pages, or the only dataset which does (FEVER) mostly consists of claims which can be fact-checked with simple reasoning and is constructed artificially. Here, we study more complex claim verification of naturally occurring claims with multiple hops over interconnected evidence chunks. We: 1) construct a small annotated dataset, PolitiHop, of evidence sentences for claim verification; 2) compare it to existing multi-hop datasets; and 3) study how to transfer knowledge from more extensive in- and out-of-domain resources to PolitiHop. We find that the task is complex and achieve the best performance with an architecture that specifically models reasoning over evidence pieces in combination with in-domain transfer learning.},
  archiveprefix = {arXiv},
  keywords = {Computer Science - Artificial Intelligence,Computer Science - Computation and Language,Computer Science - Machine Learning},
  annotation = {Read\_Status: Not Reading\\
Read\_Status\_Date: 2025-07-28T14:49:44.031Z},
  file = {C\:\\Users\\nasty\\Documents\\Zotero\\Ostrowski et al. - 2021 - Multi-Hop Fact Checking of Political Claims 1.pdf;C\:\\Users\\nasty\\Zotero\\storage\\YQDZ63AH\\2009.html}
}

@inproceedings{panayotovGREENERGraphNeural2022,
  title = {{{GREENER}}: {{Graph Neural Networks}} for {{News Media Profiling}}},
  shorttitle = {{{GREENER}}},
  booktitle = {Proceedings of the 2022 {{Conference}} on {{Empirical Methods}} in {{Natural Language Processing}}},
  author = {Panayotov, Panayot and Shukla, Utsav and Sencar, Husrev Taha and Nabeel, Mohamed and Nakov, Preslav},
  editor = {Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue},
  year = 2022,
  month = dec,
  pages = {7470--7480},
  publisher = {Association for Computational Linguistics},
  address = {Abu Dhabi, United Arab Emirates},
  doi = {10.18653/v1/2022.emnlp-main.506},
  urldate = {2024-12-02},
  abstract = {We study the problem of profiling news media on the Web with respect to their factuality of reporting and bias. This is an important but under-studied problem related to disinformation and ``fake news'' detection, but it addresses the issue at a coarser granularity compared to looking at an individual article or an individual claim. This is useful as it allows to profile entire media outlets in advance. Unlike previous work, which has focused primarily on text (e.g., on the text of the articles published by the target website, or on the textual description in their social media profiles or in Wikipedia), here our main focus is on modeling the similarity between media outlets based on the overlap of their audience. This is motivated by homophily considerations, i.e., the tendency of people to have connections to people with similar interests, which we extend to media, hypothesizing that similar types of media would be read by similar kinds of users. In particular, we propose GREENER (GRaph nEural nEtwork for News mEdia pRofiling), a model that builds a graph of inter-media connections based on their audience overlap, and then uses graph neural networks to represent each medium. We find that such representations are quite useful for predicting the factuality and the bias of news media outlets, yielding improvements over state-of-the-art results reported on two datasets. When augmented with conventionally used representations obtained from news articles, Twitter, YouTube, Facebook, and Wikipedia, prediction accuracy is found to improve by 2.5-27 macro-F1 points for the two tasks.},
  annotation = {Read\_Status: To Read\\
Read\_Status\_Date: 2025-07-28T14:51:26.394Z},
  file = {C:\Users\nasty\Documents\Zotero\Panayotov et al. - 2022 - GREENER Graph Neural Networks for News Media Profiling 1.pdf}
}

@inproceedings{pandaImprovingCrossdomainCrosslingual2022,
  title = {Improving {{Cross-domain}}, {{Cross-lingual}} and {{Multi-modal Deception Detection}}},
  booktitle = {Proceedings of the 60th {{Annual Meeting}} of the {{Association}} for {{Computational Linguistics}}: {{Student Research Workshop}}},
  author = {Panda, Subhadarshi and Levitan, Sarah Ita},
  editor = {Louvan, Samuel and Madotto, Andrea and Madureira, Brielen},
  year = 2022,
  month = may,
  pages = {383--390},
  publisher = {Association for Computational Linguistics},
  address = {Dublin, Ireland},
  doi = {10.18653/v1/2022.acl-srw.30},
  urldate = {2025-01-06},
  abstract = {With the increase of deception and misinformation especially in social media, it has become crucial to be able to develop machine learning methods to automatically identify deceptive language. In this proposal, we identify key challenges underlying deception detection in cross-domain, cross-lingual and multi-modal settings. To improve cross-domain deception classification, we propose to use inter-domain distance to identify a suitable source domain for a given target domain. We propose to study the efficacy of multilingual classification models vs translation for cross-lingual deception classification. Finally, we propose to better understand multi-modal deception detection and explore methods to weight and combine information from multiple modalities to improve multi-modal deception classification.},
  annotation = {Read\_Status: Not Reading\\
Read\_Status\_Date: 2025-08-25T14:33:21.237Z},
  file = {C:\Users\nasty\Documents\Zotero\Panda and Levitan - 2022 - Improving Cross-domain, Cross-lingual and Multi-modal Deception Detection.pdf}
}

@misc{parkChallengesOpportunitiesInformation2022,
  title = {Challenges and {{Opportunities}} in {{Information Manipulation Detection}}: {{An Examination}} of {{Wartime Russian Media}}},
  shorttitle = {Challenges and {{Opportunities}} in {{Information Manipulation Detection}}},
  author = {Park, Chan Young and Mendelsohn, Julia and Field, Anjalie and Tsvetkov, Yulia},
  year = 2022,
  month = oct,
  number = {arXiv:2205.12382},
  eprint = {2205.12382},
  primaryclass = {cs},
  publisher = {arXiv},
  doi = {10.48550/arXiv.2205.12382},
  urldate = {2024-12-10},
  abstract = {NLP research on public opinion manipulation campaigns has primarily focused on detecting overt strategies such as fake news and disinformation. However, information manipulation in the ongoing Russia-Ukraine war exemplifies how governments and media also employ more nuanced strategies. We release a new dataset, VoynaSlov, containing 38M+ posts from Russian media outlets on Twitter and VKontakte, as well as public activity and responses, immediately preceding and during the 2022 Russia-Ukraine war. We apply standard and recently-developed NLP models on VoynaSlov to examine agenda setting, framing, and priming, several strategies underlying information manipulation, and reveal variation across media outlet control, social media platform, and time. Our examination of these media effects and extensive discussion of current approaches' limitations encourage further development of NLP models for understanding information manipulation in emerging crises, as well as other real-world and interdisciplinary tasks.},
  archiveprefix = {arXiv},
  langid = {english},
  keywords = {Computer Science - Computation and Language},
  annotation = {Read\_Status: Read\\
Read\_Status\_Date: 2025-07-03T14:42:50.060Z},
  file = {C\:\\Users\\nasty\\Documents\\Zotero\\Park et al. - 2022 - Challenges and Opportunities in Information Manipulation Detection An Examination of Wartime Russia.pdf;C\:\\Users\\nasty\\Documents\\Zotero\\Park et al. - 2022 - Challenges and Opportunities in Information Manipulation Detection An Examination of Wartime Russia.pdf}
}

@inproceedings{peritiAnalyzingSemanticChange2024,
  title = {Analyzing {{Semantic Change}} through {{Lexical Replacements}}},
  booktitle = {Proceedings of the 62nd {{Annual Meeting}} of the {{Association}} for {{Computational Linguistics}} ({{Volume}} 1: {{Long Papers}})},
  author = {Periti, Francesco and Cassotti, Pierluigi and Dubossarsky, Haim and Tahmasebi, Nina},
  editor = {Ku, Lun-Wei and Martins, Andre and Srikumar, Vivek},
  year = 2024,
  month = aug,
  pages = {4495--4510},
  publisher = {Association for Computational Linguistics},
  address = {Bangkok, Thailand},
  doi = {10.18653/v1/2024.acl-long.246},
  urldate = {2025-04-02},
  abstract = {Modern language models are capable of contextualizing words based on their surrounding context. However, this capability is often compromised due to semantic change that leads to words being used in new, unexpected contexts not encountered during pre-training. In this paper, we model semantic change by studying the effect of unexpected contexts introduced by lexical replacements. We propose a replacement schema where a target word is substituted with lexical replacements of varying relatedness, thus simulating different kinds of semantic change. Furthermore, we leverage the replacement schema as a basis for a novel interpretable model for semantic change. We are also the first to evaluate the use of LLaMa for semantic change detection.},
  annotation = {Read\_Status: To Read\\
Read\_Status\_Date: 2025-07-28T14:54:08.346Z},
  file = {C:\Users\nasty\Documents\Zotero\Periti et al. - 2024 - Analyzing Semantic Change through Lexical Replacements.pdf}
}

@article{peritiLexicalSemanticChange2024,
  title = {Lexical {{Semantic Change}} through {{Large Language Models}}: A {{Survey}}},
  shorttitle = {Lexical {{Semantic Change}} through {{Large Language Models}}},
  author = {Periti, Francesco and Montanelli, Stefano},
  year = 2024,
  month = jun,
  journal = {ACM Comput. Surv.},
  volume = {56},
  number = {11},
  pages = {282:1--282:38},
  issn = {0360-0300},
  doi = {10.1145/3672393},
  urldate = {2024-11-07},
  abstract = {Lexical Semantic Change (LSC) is the task of identifying, interpreting, and assessing the possible change over time in the meanings of a target word. Traditionally, LSC has been addressed by linguists and social scientists through manual and time-consuming analyses, which have thus been limited in terms of the volume, genres, and time-frame that can be considered. In recent years, computational approaches based on Natural Language Processing have gained increasing attention to automate LSC as much as possible. Significant advancements have been made by relying on Large Language Models (LLMs), which can handle the multiple usages of the words and better capture the related semantic change. In this article, we survey the approaches based on LLMs for LSC, and we propose a classification framework characterized by three dimensions: meaning representation, time-awareness, and learning modality. The framework is exploited to (i) review the measures for change assessment, (ii) compare the approaches on performance, and (iii) discuss the current issues in terms of scalability, interpretability, and robustness. Open challenges and future research directions about the use of LLMs for LSC are finally outlined.},
  keywords = {cascade_reading_group,example_survey},
  annotation = {Read\_Status: Read\\
Read\_Status\_Date: 2025-07-28T14:55:33.198Z},
  file = {C:\Users\nasty\Documents\Zotero\Periti and Montanelli - 2024 - Lexical Semantic Change through Large Language Models a Survey 1.pdf}
}

@article{peritiStudyingWordMeaning2024,
  title = {Studying Word Meaning Evolution through Incremental Semantic Shift Detection},
  author = {Periti, Francesco and Picascia, Sergio and Montanelli, Stefano and Ferrara, Alfio and Tahmasebi, Nina},
  year = 2024,
  month = sep,
  journal = {Language Resources and Evaluation},
  issn = {1574-0218},
  doi = {10.1007/s10579-024-09769-1},
  urldate = {2025-02-15},
  abstract = {The study of semantic shift, that is, of how words change meaning as a consequence of social practices, events and political circumstances, is relevant in Natural Language Processing, Linguistics, and Social Sciences. The increasing availability of large diachronic corpora and advance in computational semantics have accelerated the development of computational approaches to detecting such shift. In this paper, we introduce a novel approach to tracing the evolution of word meaning over time. Our analysis focuses on gradual changes in word semantics and relies on an incremental approach to semantic shift detection (SSD) called What is Done is Done (WiDiD). WiDiD leverages scalable and evolutionary clustering of contextualised word embeddings to detect semantic shift and capture temporal transactions in word meanings. Existing approaches to SSD: (a) significantly simplify the semantic shift problem to cover change between two (or a few) time points, and (b) consider the existing corpora as static. We instead treat SSD as an organic process in which word meanings evolve across tens or even hundreds of time periods as the corpus is progressively made available. This results in an extremely demanding task that entails a multitude of intricate decisions. We demonstrate the applicability of this incremental approach on a diachronic corpus of Italian parliamentary speeches spanning eighteen distinct time periods. We also evaluate its performance on seven popular labelled benchmarks for SSD across multiple languages. Empirical results show that our results are comparable to state-of-the-art approaches, while outperforming the state-of-the-art for certain languages.},
  langid = {english},
  keywords = {cascade_reading_group,Contextualized word embeddings,Evolutionary clustering,Lexical semantic change,Semantic shift detection},
  annotation = {Read\_Status: Read\\
Read\_Status\_Date: 2025-07-28T14:57:33.584Z},
  file = {C\:\\Users\\nasty\\Documents\\Zotero\\Periti et al. - 2024 - Studying word meaning evolution through incremental semantic shift detection 1.pdf;C\:\\Users\\User\\OneDrive - Universität des Saarlandes\\Documents\\Zotero\\Periti et al. - 2024 - Studying word meaning evolution through incremental semantic shift detection.pdf}
}

@article{piantadosiWordLengthsAre2011,
  title = {Word Lengths Are Optimized for Efficient Communication},
  author = {Piantadosi, Steven T. and Tily, Harry and Gibson, Edward},
  year = 2011,
  month = mar,
  journal = {Proceedings of the National Academy of Sciences},
  volume = {108},
  number = {9},
  pages = {3526--3529},
  issn = {0027-8424, 1091-6490},
  doi = {10.1073/pnas.1012551108},
  urldate = {2024-10-01},
  abstract = {We demonstrate a substantial improvement on one of the most celebrated empirical laws in the study of language, Zipf's 75-y-old theory that word length is primarily determined by frequency of use. In accord with rational theories of communication, we show across 10 languages that average information content is a much better predictor of word length than frequency. This indicates that human lexicons are efficiently structured for communication by taking into account interword statistical dependencies. Lexical systems result from an optimization of communicative pressures, coding meanings efficiently given the complex statistics of natural language use.},
  langid = {english},
  annotation = {Read\_Status: Read\\
Read\_Status\_Date: 2025-07-28T15:03:17.660Z},
  file = {C:\Users\nasty\Documents\Zotero\Piantadosi et al. - 2011 - Word lengths are optimized for efficient communication 1.pdf}
}

@article{piaoTimesensitiveHistoricalThesaurusbased2017,
  title = {A Time-Sensitive Historical Thesaurus-Based Semantic Tagger for Deep Semantic Annotation},
  author = {Piao, Scott and Dallachy, Fraser and Baron, Alistair and Demmen, Jane and Wattam, Steve and Durkin, Philip and McCracken, James and Rayson, Paul and Alexander, Marc},
  year = 2017,
  month = nov,
  journal = {Computer Speech \& Language},
  volume = {46},
  pages = {113--135},
  issn = {08852308},
  doi = {10.1016/j.csl.2017.04.010},
  urldate = {2024-10-15},
  langid = {english},
  annotation = {Read\_Status: Not Reading\\
Read\_Status\_Date: 2025-07-28T15:06:07.786Z},
  file = {C\:\\Users\\nasty\\Documents\\Zotero\\Piao et al. - 2017 - A time-sensitive historical thesaurus-based semantic tagger for deep semantic annotation 2.pdf;C\:\\Users\\User\\OneDrive - Universität des Saarlandes\\Documents\\Zotero\\Piao et al. - 2017 - A time-sensitive historical thesaurus-based semantic tagger for deep semantic annotation.pdf}
}

@article{piskorskiExploringDataAugmentation,
  title = {Exploring {{Data Augmentation}} for {{Classification}} of {{Climate Change Denial}}: {{Preliminary Study}}},
  author = {Piskorski, Jakub and Nikolaidis, Nikolaos and Stefanovitch, Nicolas and Kotseva, Bonka and Vianini, Irene and Kharazi, Sopho and Linge, Jens P},
  abstract = {In order to address the growing need of monitoring climate-change denial narratives in online sources, NLP-based methods have the potential to automate this process. Here, we report on preliminary experiments of exploiting Data Augmentation techniques for improving climate change denial classification. We focus on a selection of both known techniques, and augmentation transformations not reported elsewhere that replace certain type of named entities with high probability of preserving labels. We also introduce a new benchmark dataset consisting of text snippets extracted from online news labeled with fine-grained climate change denial types.},
  langid = {english},
  annotation = {Read\_Status: Not Reading\\
Read\_Status\_Date: 2025-07-28T15:06:23.229Z},
  file = {C\:\\Users\\nasty\\Documents\\Zotero\\Piskorski et al. - Exploring Data Augmentation for Classification of Climate Change Denial Preliminary Study 1.pdf;C\:\\Users\\nasty\\Documents\\Zotero\\Piskorski et al. - Exploring Data Augmentation for Classification of Climate Change Denial Preliminary Study 2.pdf;C\:\\Users\\User\\OneDrive - Universität des Saarlandes\\Documents\\Zotero\\Piskorski et al. - Exploring Data Augmentation for Classification of Climate Change Denial Preliminary Study.pdf}
}

@inproceedings{pivovarovaRuShiftEvalSharedTask2021,
  title = {{{RuShiftEval}}: A Shared Task on Semantic Shift Detection for {{Russian}}},
  shorttitle = {{{RuShiftEval}}},
  booktitle = {Computational {{Linguistics}} and {{Intellectual Technologies}}},
  author = {Pivovarova, Lidia and Kutuzov, Andrey},
  year = 2021,
  month = jun,
  pages = {533--545},
  doi = {10.28995/2075-7182-2021-20-533-545},
  urldate = {2024-12-16},
  abstract = {We present the first shared task on diachronic word meaning change detection for the Russian. The participating systems were provided with three sub-corpora of the Russian National Corpus --- corresponding to pre-Soviet, Soviet and post-Soviet periods respectively --- and a set of approximately one hundred Russian nouns. The task was to rank those nouns according to the degrees of their meaning change between periods.},
  langid = {english},
  annotation = {Read\_Status: To Read\\
Read\_Status\_Date: 2025-07-28T15:06:47.126Z},
  file = {C:\Users\nasty\Documents\Zotero\Pivovarova and Kutuzov - 2021 - RuShiftEval a shared task on semantic shift detection for Russian 1.pdf}
}

@article{plikynasSystematicReviewFake2025,
  title = {Systematic {{Review}} of {{Fake News}}, {{Propaganda}}, and {{Disinformation}}: {{Examining Authors}}, {{Content}}, and {{Social Impact Through Machine Learning}}},
  shorttitle = {Systematic {{Review}} of {{Fake News}}, {{Propaganda}}, and {{Disinformation}}},
  author = {Plikynas, Darius and Rizgelien{\.e}, Ieva and Korvel, Gra{\v z}ina},
  year = 2025,
  journal = {IEEE Access},
  volume = {13},
  pages = {17583--17629},
  issn = {2169-3536},
  doi = {10.1109/ACCESS.2025.3530688},
  urldate = {2025-08-25},
  abstract = {In recent years, the world has witnessed a global outbreak of fake news, propaganda and disinformation (FNPD) flows on online social networks (OSN). In the context of information warfare and the capabilities of generative AI, FNPDs have proliferated. They have become a powerful and quite effective tool for influencing people's social identities, attitudes, opinions and even behavior. Ad hoc malicious social media accounts and organized networks of trolls and bots target countries, societies, social groups, political campaigns and individuals. As a result, conspiracy theories, echo chambers, filter bubbles and other processes of fragmentation and marginalization are polarizing, radicalizing, and disintegrating society in terms of coherent politics, governance, and social networks of trust and cooperation. This systematic review aims to explore advances in using machine and deep learning to detect FNPD in OSNs effectively. We present the results of a combined PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) review in three analysis domains: 1) propagators (authors, trolls, and bots), 2) textual content, 3) social impact. This systemic research framework integrates meta-analyses of three research domains, providing an overview of the wider research field and revealing important relationships between these research domains. It not only addresses the most promising ML/DL research methodologies and hybrid approaches in each domain, but also provides perspectives and insights on future research directions.},
  keywords = {Accuracy,Analytical models,authors' analysis,Classification algorithms,content analysis,Content management,deep learning,Deep learning,fake news,Fake news,Feature extraction,Machine learning,PRISMA systematic review,propaganda and disinformation,Security,social impact analysis,Social networking (online),Systematic literature review},
  annotation = {Read\_Status: To Read\\
Read\_Status\_Date: 2025-08-25T16:32:51.303Z},
  file = {C:\Users\nasty\Documents\Zotero\Plikynas et al. - 2025 - Systematic Review of Fake News, Propaganda, and Disinformation Examining Authors, Content, and Soci.pdf}
}

@misc{PrewarExperimentalEvidence2024,
  title = {Pre-War Experimental Evidence That {{Putin}}'s Propaganda Elicited Strong Support for Military Invasion among {{Russians}} \textbar{} {{Science Advances}}},
  year = 2024,
  month = dec,
  urldate = {2024-12-13},
  howpublished = {https://www.science.org/doi/10.1126/sciadv.adg1199},
  annotation = {Read\_Status: Not Reading\\
Read\_Status\_Date: 2025-07-28T15:38:22.406Z}
}

@inproceedings{rashkinTruthVaryingShades2017,
  title = {Truth of {{Varying Shades}}: {{Analyzing Language}} in {{Fake News}} and {{Political Fact-Checking}}},
  shorttitle = {Truth of {{Varying Shades}}},
  booktitle = {Proceedings of the 2017 {{Conference}} on {{Empirical Methods}} in {{Natural}}           {{Language Processing}}},
  author = {Rashkin, Hannah and Choi, Eunsol and Jang, Jin Yea and Volkova, Svitlana and Choi, Yejin},
  year = 2017,
  pages = {2931--2937},
  publisher = {Association for Computational Linguistics},
  address = {Copenhagen, Denmark},
  doi = {10.18653/v1/D17-1317},
  urldate = {2025-08-05},
  abstract = {We present an analytic study on the language of news media in the context of political fact-checking and fake news detection. We compare the language of real news with that of satire, hoaxes, and propaganda to find linguistic characteristics of untrustworthy text. To probe the feasibility of automatic political fact-checking, we also present a case study based on PolitiFact.com using their factuality judgments on a 6-point scale. Experiments show that while media fact-checking remains to be an open research question, stylistic cues can help determine the truthfulness of text.},
  langid = {english},
  annotation = {Read\_Status: To Read\\
Read\_Status\_Date: 2025-08-25T14:33:45.443Z},
  file = {C:\Users\nasty\Documents\Zotero\Rashkin et al. - 2017 - Truth of Varying Shades Analyzing Language in Fake News and Political Fact-Checking.pdf}
}

@misc{riedelSimpleToughtobeatBaseline2018,
  title = {A Simple but Tough-to-Beat Baseline for the {{Fake News Challenge}} Stance Detection Task},
  author = {Riedel, Benjamin and Augenstein, Isabelle and Spithourakis, Georgios P. and Riedel, Sebastian},
  year = 2018,
  month = may,
  number = {arXiv:1707.03264},
  eprint = {1707.03264},
  publisher = {arXiv},
  doi = {10.48550/arXiv.1707.03264},
  urldate = {2024-11-21},
  abstract = {Identifying public misinformation is a complicated and challenging task. An important part of checking the veracity of a specific claim is to evaluate the stance different news sources take towards the assertion. Automatic stance evaluation, i.e. stance detection, would arguably facilitate the process of fact checking. In this paper, we present our stance detection system which claimed third place in Stage 1 of the Fake News Challenge. Despite our straightforward approach, our system performs at a competitive level with the complex ensembles of the top two winning teams. We therefore propose our system as the 'simple but tough-to-beat baseline' for the Fake News Challenge stance detection task.},
  archiveprefix = {arXiv},
  keywords = {Computer Science - Computation and Language},
  annotation = {Read\_Status: Not Reading\\
Read\_Status\_Date: 2025-07-28T15:07:19.612Z},
  file = {C\:\\Users\\nasty\\Documents\\Zotero\\Riedel et al. - 2018 - A simple but tough-to-beat baseline for the Fake News Challenge stance detection task 1.pdf;C\:\\Users\\nasty\\Zotero\\storage\\PIRM3DKU\\1707.html}
}

@article{roytburgWordsActionModeling2025,
  title = {Words and {{Action}}: {{Modeling Linguistic Leadership}} in \#{{BlackLivesMatter Communities}}},
  shorttitle = {Words and {{Action}}},
  author = {Roytburg, Dani and Olorunisola, Deborah and Soni, Sandeep and Klein, Lauren},
  year = 2025,
  month = jun,
  journal = {Proceedings of the International AAAI Conference on Web and Social Media},
  volume = {19},
  pages = {1690--1703},
  issn = {2334-0770},
  doi = {10.1609/icwsm.v19i1.35895},
  urldate = {2025-07-01},
  abstract = {In the wake of the 2024 US presidential election, pundits on both the left and the right pointed to a conservative backlash against "woke politics" to explain the election's outcome. These politics, rooted in substantive beliefs about equity and justice--and particularly racial justice--owe their most recent rise to prominence to the Black Lives Matter (BLM) movement. A significant body of work, both qualitative and quantitative, has documented how BLM was able to move these beliefs from the margin to the mainstream. In this paper, we focus on the words that index these beliefs, devising a novel method of modeling semantic leadership across a set of communities associated with the BLM movement that is informed by domain-specific theory about Black Twitter. We describe our bespoke approaches to time-binning, community clustering, and connecting communities over time, as well as our adaptation of state-of-the-art approaches to semantic change detection and semantic leadership induction. We find evidence at scale of the leadership role of BLM activists and progressives, as well as of Black celebrities. We also find evidence of sustained conservative engagement with this discourse, suggesting an alternative explanation for how we have arrived at the present political moment.},
  copyright = {Copyright (c) 2025 Association for the Advancement of Artificial Intelligence},
  langid = {english},
  keywords = {icwsm2025},
  annotation = {Read\_Status: To Read\\
Read\_Status\_Date: 2025-07-03T15:09:15.731Z},
  file = {C:\Users\nasty\Documents\Zotero\Roytburg et al. - 2025 - Words and Action Modeling Linguistic Leadership in #BlackLivesMatter Communities.pdf}
}

@inproceedings{sahitajHybridAnnotationPropaganda2025,
  title = {Hybrid {{Annotation}} for {{Propaganda Detection}}: {{Integrating LLM Pre-Annotations}} with {{Human Intelligence}}},
  shorttitle = {Hybrid {{Annotation}} for {{Propaganda Detection}}},
  booktitle = {Proceedings of the {{Fourth Workshop}} on {{NLP}} for {{Positive Impact}} ({{NLP4PI}})},
  author = {Sahitaj, Ariana and Sahitaj, Premtim and Solopova, Veronika and Li, Jiaao and M{\"o}ller, Sebastian and Schmitt, Vera},
  editor = {Atwell, Katherine and Biester, Laura and Borah, Angana and Dementieva, Daryna and Ignat, Oana and Kotonya, Neema and Liu, Ziyi and Wan, Ruyuan and Wilson, Steven and Zhao, Jieyu},
  year = 2025,
  month = jul,
  pages = {215--228},
  publisher = {Association for Computational Linguistics},
  address = {Vienna, Austria},
  urldate = {2025-08-04},
  abstract = {Propaganda detection on social media remains challenging due to task complexity and limited high-quality labeled data. This paper introduces a novel framework that combines human expertise with Large Language Model (LLM) assistance to improve both annotation consistency and scalability. We propose a hierarchical taxonomy that organizes 14 fine-grained propaganda techniques (CITATION) into three broader categories, conduct a human annotation study on the HQP dataset (CITATION) that reveals low inter-annotator agreement for fine-grained labels, and implement an LLM-assisted pre-annotation pipeline that extracts propagandistic spans, generates concise explanations, and assigns local labels as well as a global label. A secondary human verification study shows significant improvements in both agreement and time-efficiency. Building on this, we fine-tune smaller language models (SLMs) to perform structured annotation. Instead of fine-tuning on human annotations, we train on high-quality LLM-generated data, allowing a large model to produce these annotations and a smaller model to learn to generate them via knowledge distillation. Our work contributes towards the development of scalable and robust propaganda detection systems, supporting the idea of transparent and accountable media ecosystems in line with SDG 16. The code is publicly available at our GitHub repository.},
  isbn = {978-1-959429-19-7},
  annotation = {Read\_Status: To Read\\
Read\_Status\_Date: 2025-08-20T13:44:12.091Z}
}

@article{scheufeleFramingAgendaSetting2007,
  title = {Framing, {{Agenda Setting}}, and {{Priming}}: {{The Evolution}} of {{Three Media Effects Models}}: {{Models}} of {{Media Effects}}},
  shorttitle = {Framing, {{Agenda Setting}}, and {{Priming}}},
  author = {Scheufele, Dietram A. and Tewksbury, David},
  year = 2007,
  month = mar,
  journal = {Journal of Communication},
  volume = {57},
  number = {1},
  pages = {9--20},
  issn = {00219916},
  doi = {10.1111/j.0021-9916.2007.00326.x},
  urldate = {2025-07-28},
  copyright = {http://doi.wiley.com/10.1002/tdm\_license\_1.1},
  langid = {english},
  annotation = {Read\_Status: Not Reading\\
Read\_Status\_Date: 2025-07-28T15:10:44.435Z},
  file = {C\:\\Users\\nasty\\Documents\\Zotero\\Scheufele and Tewksbury - 2007 - Framing, Agenda Setting, and Priming The Evolution of Three Media Effects Models Models of Media E.pdf;C\:\\Users\\User\\OneDrive - Universität des Saarlandes\\Documents\\Zotero\\Scheufele and Tewksbury - 2007 - Framing, Agenda Setting, and Priming The Evolution of Three Media Effects Models Models of Media E.pdf}
}

@inproceedings{schlechtwegSemEval2020Task12020,
  title = {{{SemEval-2020 Task}} 1: {{Unsupervised Lexical Semantic Change Detection}}},
  shorttitle = {{{SemEval-2020 Task}} 1},
  booktitle = {Proceedings of the {{Fourteenth Workshop}} on {{Semantic Evaluation}}},
  author = {Schlechtweg, Dominik and McGillivray, Barbara and Hengchen, Simon and Dubossarsky, Haim and Tahmasebi, Nina},
  editor = {Herbelot, Aurelie and Zhu, Xiaodan and Palmer, Alexis and Schneider, Nathan and May, Jonathan and Shutova, Ekaterina},
  year = 2020,
  month = dec,
  pages = {1--23},
  publisher = {International Committee for Computational Linguistics},
  address = {Barcelona (online)},
  doi = {10.18653/v1/2020.semeval-1.1},
  urldate = {2024-11-07},
  abstract = {Lexical Semantic Change detection, i.e., the task of identifying words that change meaning over time, is a very active research area, with applications in NLP, lexicography, and linguistics. Evaluation is currently the most pressing problem in Lexical Semantic Change detection, as no gold standards are available to the community, which hinders progress. We present the results of the first shared task that addresses this gap by providing researchers with an evaluation framework and manually annotated, high-quality datasets for English, German, Latin, and Swedish. 33 teams submitted 186 systems, which were evaluated on two subtasks.},
  annotation = {Read\_Status: To Read\\
Read\_Status\_Date: 2025-07-28T15:12:56.818Z},
  file = {C\:\\Users\\nasty\\Documents\\Zotero\\Schlechtweg et al. - 2020 - SemEval-2020 Task 1 Unsupervised Lexical Semantic Change Detection 2.pdf;C\:\\Users\\User\\OneDrive - Universität des Saarlandes\\Documents\\Zotero\\Schlechtweg et al. - 2020 - SemEval-2020 Task 1 Unsupervised Lexical Semantic Change Detection.pdf}
}

@inproceedings{schlechtwegWindChangeDetecting2019,
  title = {A {{Wind}} of {{Change}}: {{Detecting}} and {{Evaluating Lexical Semantic Change}} across {{Times}} and {{Domains}}},
  shorttitle = {A {{Wind}} of {{Change}}},
  booktitle = {Proceedings of the 57th {{Annual Meeting}} of the {{Association}} for {{Computational Linguistics}}},
  author = {Schlechtweg, Dominik and H{\"a}tty, Anna and Del Tredici, Marco and {Schulte im Walde}, Sabine},
  editor = {Korhonen, Anna and Traum, David and M{\`a}rquez, Llu{\'i}s},
  year = 2019,
  month = jul,
  pages = {732--746},
  publisher = {Association for Computational Linguistics},
  address = {Florence, Italy},
  doi = {10.18653/v1/P19-1072},
  urldate = {2024-11-07},
  abstract = {We perform an interdisciplinary large-scale evaluation for detecting lexical semantic divergences in a diachronic and in a synchronic task: semantic sense changes across time, and semantic sense changes across domains. Our work addresses the superficialness and lack of comparison in assessing models of diachronic lexical change, by bringing together and extending benchmark models on a common state-of-the-art evaluation task. In addition, we demonstrate that the same evaluation task and modelling approaches can successfully be utilised for the synchronic detection of domain-specific sense divergences in the field of term extraction.},
  annotation = {Read\_Status: Read\\
Read\_Status\_Date: 2025-07-28T15:12:34.626Z},
  file = {C\:\\Users\\nasty\\Documents\\Zotero\\Schlechtweg et al. - 2019 - A Wind of Change Detecting and Evaluating Lexical Semantic Change across Times and Domains 1.pdf;C\:\\Users\\nasty\\Documents\\Zotero\\Schlechtweg et al. - 2019 - A Wind of Change Detecting and Evaluating Lexical Semantic Change across Times and Domains 2.pdf;C\:\\Users\\User\\OneDrive - Universität des Saarlandes\\Documents\\Zotero\\Schlechtweg et al. - 2019 - A Wind of Change Detecting and Evaluating Lexical Semantic Change across Times and Domains.pdf}
}

@misc{shaarThatKnownLie2020,
  title = {That Is a {{Known Lie}}: {{Detecting Previously Fact-Checked Claims}}},
  shorttitle = {That Is a {{Known Lie}}},
  author = {Shaar, Shaden and Martino, Giovanni Da San and Babulkov, Nikolay and Nakov, Preslav},
  year = 2020,
  month = may,
  number = {arXiv:2005.06058},
  eprint = {2005.06058},
  primaryclass = {cs},
  publisher = {arXiv},
  doi = {10.48550/arXiv.2005.06058},
  urldate = {2025-08-13},
  abstract = {The recent proliferation of "fake news" has triggered a number of responses, most notably the emergence of several manual fact-checking initiatives. As a result and over time, a large number of fact-checked claims have been accumulated, which increases the likelihood that a new claim in social media or a new statement by a politician might have already been fact-checked by some trusted fact-checking organization, as viral claims often come back after a while in social media, and politicians like to repeat their favorite statements, true or false, over and over again. As manual fact-checking is very time-consuming (and fully automatic fact-checking has credibility issues), it is important to try to save this effort and to avoid wasting time on claims that have already been fact-checked. Interestingly, despite the importance of the task, it has been largely ignored by the research community so far. Here, we aim to bridge this gap. In particular, we formulate the task and we discuss how it relates to, but also differs from, previous work. We further create a specialized dataset, which we release to the research community. Finally, we present learning-to-rank experiments that demonstrate sizable improvements over state-of-the-art retrieval and textual similarity approaches.},
  archiveprefix = {arXiv},
  keywords = {Computer Science - Computation and Language,Computer Science - Information Retrieval,Computer Science - Machine Learning,Computer Science - Social and Information Networks},
  annotation = {Read\_Status: To Read\\
Read\_Status\_Date: 2025-08-20T13:45:27.220Z},
  file = {C\:\\Users\\nasty\\Documents\\Zotero\\Shaar et al. - 2020 - That is a Known Lie Detecting Previously Fact-Checked Claims.pdf;C\:\\Users\\nasty\\Zotero\\storage\\CTVMX4TW\\2005.html}
}

@article{shannonMathematicalTheoryCommunication1948,
  title = {A {{Mathematical Theory}} of {{Communication}}},
  author = {Shannon, Claude E.},
  year = 1948,
  journal = {Bell System Technical Journal},
  volume = {27},
  number = {3},
  pages = {379--423},
  issn = {1538-7305},
  doi = {10.1002/j.1538-7305.1948.tb01338.x},
  urldate = {2025-03-27},
  copyright = {\copyright{} 1948 The Bell System Technical Journal},
  langid = {english},
  annotation = {Read\_Status: Not Reading\\
Read\_Status\_Date: 2025-07-28T15:13:05.794Z},
  file = {C\:\\Users\\User\\OneDrive - Universität des Saarlandes\\Documents\\Zotero\\Shannon - A Mathematical Theory of Communication.pdf;C\:\\Users\\nasty\\Zotero\\storage\\LMYQ7UK9\\j.1538-7305.1948.tb01338.html;C\:\\Users\\nasty\\Zotero\\storage\\RHMTN92X\\j.1538-7305.1948.tb01338.html}
}

@book{shoemakerGatekeepingTheory2009,
  title = {Gatekeeping {{Theory}}},
  author = {Shoemaker, Pamela J. and Vos, Timothy},
  year = 2009,
  month = sep,
  publisher = {Routledge},
  address = {New York},
  doi = {10.4324/9780203931653},
  abstract = {Gatekeeping is one of the media's central roles in public life: people rely on mediators to transform information about billions of events into a manageable number of media messages. This process determines not only which information is selected, but also what the content and nature of messages, such as news, will be. Gatekeeping Theory describes the powerful process through which events are covered by the mass media, explaining how and why certain information either passes through gates or is closed off from media attention. This book is essential for understanding how even single, seemingly trivial gatekeeping decisions can come together to shape an audience's view of the world, and illustrates what is at stake in the process.},
  isbn = {978-0-203-93165-3},
  annotation = {Read\_Status: New\\
Read\_Status\_Date: 2025-10-29T17:38:22.620Z}
}

@inproceedings{shoemarkRoomGloSystematic2019,
  title = {Room to {{Glo}}: {{A Systematic Comparison}} of {{Semantic Change Detection Approaches}} with {{Word Embeddings}}},
  shorttitle = {Room to {{Glo}}},
  booktitle = {Proceedings of the 2019 {{Conference}} on {{Empirical Methods}} in {{Natural Language Processing}} and the 9th {{International Joint Conference}} on {{Natural Language Processing}} ({{EMNLP-IJCNLP}})},
  author = {Shoemark, Philippa and Liza, Farhana Ferdousi and Nguyen, Dong and Hale, Scott and McGillivray, Barbara},
  editor = {Inui, Kentaro and Jiang, Jing and Ng, Vincent and Wan, Xiaojun},
  year = 2019,
  month = nov,
  pages = {66--76},
  publisher = {Association for Computational Linguistics},
  address = {Hong Kong, China},
  doi = {10.18653/v1/D19-1007},
  urldate = {2024-12-18},
  abstract = {Word embeddings are increasingly used for the automatic detection of semantic change; yet, a robust evaluation and systematic comparison of the choices involved has been lacking. We propose a new evaluation framework for semantic change detection and find that (i) using the whole time series is preferable over only comparing between the first and last time points; (ii) independently trained and aligned embeddings perform better than continuously trained embeddings for long time periods; and (iii) that the reference point for comparison matters. We also present an analysis of the changes detected on a large Twitter dataset spanning 5.5 years.},
  annotation = {Read\_Status: To Read\\
Read\_Status\_Date: 2025-07-28T15:13:37.360Z},
  file = {C:\Users\nasty\Documents\Zotero\Shoemark et al. - 2019 - Room to Glo A Systematic Comparison of Semantic Change Detection Approaches with Word Embeddings 1.pdf}
}

@article{silcockMassiveScaleSemantic,
  title = {A {{Massive Scale Semantic Similarity Dataset}} of {{Historical English}}},
  author = {Silcock, Emily and Arora, Abhishek and Dell, Melissa},
  abstract = {A diversity of tasks use language models trained on semantic similarity data. While there are a variety of datasets that capture semantic similarity, they are either constructed from modern web data or are relatively small datasets created in the past decade by human annotators. This study utilizes a novel source, newly digitized articles from off-copyright, local U.S. newspapers, to assemble a massive-scale semantic similarity dataset spanning 70 years from 1920 to 1989 and containing nearly 400M positive semantic similarity pairs. Historically, around half of articles in U.S. local newspapers came from newswires like the Associated Press. While local papers reproduced articles from the newswire, they wrote their own headlines, which form abstractive summaries of the associated articles. We associate articles and their headlines by exploiting document layouts and language understanding. We then use deep neural methods to detect which articles are from the same underlying source, in the presence of substantial noise and abridgement. The headlines of reproduced articles form positive semantic similarity pairs. The resulting publicly available HEADLINES dataset is significantly larger than most existing semantic similarity datasets and covers a much longer span of time. It will facilitate the application of contrastively trained semantic similarity models to a variety of tasks, including the study of semantic change across space and time.},
  langid = {english},
  keywords = {cascade_reading_group},
  annotation = {Read\_Status: To Read\\
Read\_Status\_Date: 2025-07-28T15:01:32.974Z},
  file = {C:\Users\nasty\Documents\Zotero\Silcock et al. - A Massive Scale Semantic Similarity Dataset of Historical English.pdf}
}

@article{solopovaAutomatedMultilingualDetection2023,
  title = {Automated {{Multilingual Detection}} of {{Pro-Kremlin Propaganda}} in {{Newspapers}} and {{Telegram Posts}}},
  author = {Solopova, Veronika and Popescu, Oana-Iuliana and Benzm{\"u}ller, Christoph and Landgraf, Tim},
  year = 2023,
  month = mar,
  journal = {Datenbank-Spektrum},
  volume = {23},
  number = {1},
  pages = {5--14},
  issn = {1610-1995},
  doi = {10.1007/s13222-023-00437-2},
  urldate = {2024-12-12},
  abstract = {The full-scale conflict between the Russian Federation and Ukraine generated an unprecedented amount of news articles and social media data reflecting opposing ideologies and narratives. These polarized campaigns have led to mutual accusations of misinformation and fake news, shaping an atmosphere of confusion and mistrust for readers worldwide. This study analyses how the media affected and mirrored public opinion during the first month of the war using news articles and Telegram news channels in Ukrainian, Russian, Romanian, French and English. We propose and compare two methods of multilingual automated pro-Kremlin propaganda identification, based on Transformers and linguistic features. We analyse the advantages and disadvantages of both methods, their adaptability to new genres and languages, and ethical considerations of their usage for content moderation. With this work, we aim to lay the foundation for further development of moderation tools tailored to the current conflict.},
  langid = {english},
  keywords = {Automated Content Moderation,Fake news,Kremlin,NLP,Propaganda,Ukraine},
  annotation = {Read\_Status: To Read\\
Read\_Status\_Date: 2025-07-03T14:47:48.468Z},
  file = {C:\Users\nasty\Documents\Zotero\Solopova et al. - 2023 - Automated Multilingual Detection of Pro-Kremlin Propaganda in Newspapers and Telegram Posts 1.pdf}
}

@inproceedings{solopovaCheckNewsOne2024,
  title = {Check {{News}} in {{One Click}}: {{NLP-Empowered Pro-Kremlin Propaganda Detection}}},
  shorttitle = {Check {{News}} in {{One Click}}},
  booktitle = {Proceedings of the 18th {{Conference}} of the {{European Chapter}} of the {{Association}} for {{Computational Linguistics}}: {{System Demonstrations}}},
  author = {Solopova, Veronika and Herman, Viktoriia and Benzm{\"u}ller, Christoph and Landgraf, Tim},
  editor = {Aletras, Nikolaos and De Clercq, Orphee},
  year = 2024,
  month = mar,
  pages = {44--51},
  publisher = {Association for Computational Linguistics},
  address = {St. Julians, Malta},
  urldate = {2024-10-08},
  abstract = {Many European citizens become targets of the Kremlin propaganda campaigns, aiming to minimise public support for Ukraine, foster a climate of mistrust and disunity, and shape elections (Meister, 2022). To address this challenge, we developed ``Check News in 1 Click'', the first NLP-empowered pro-Kremlin propaganda detection application available in 7 languages, which provides the lay user with feedback on their news, and explains manipulative linguistic features and keywords. We conducted a user study, analysed user entries and models' behaviour paired with questionnaire answers, and investigated the advantages and disadvantages of the proposed interpretative solution.},
  annotation = {Read\_Status: Read\\
Read\_Status\_Date: 2025-07-03T14:43:29.267Z},
  file = {C\:\\Users\\nasty\\Documents\\Zotero\\Solopova et al. - 2024 - Check News in One Click NLP-Empowered Pro-Kremlin Propaganda Detection 1.pdf;C\:\\Users\\nasty\\Documents\\Zotero\\Solopova et al. - 2024 - Check News in One Click NLP-Empowered Pro-Kremlin Propaganda Detection 1.pdf;C\:\\Users\\nasty\\Documents\\Zotero\\Solopova et al. - 2024 - Check News in One Click NLP-Empowered Pro-Kremlin Propaganda Detection 2.pdf}
}

@inproceedings{solopovaEvolutionProKremlinPropaganda2023,
  title = {The {{Evolution}} of {{Pro-Kremlin Propaganda From}} a {{Machine Learning}} and {{Linguistics Perspective}}},
  booktitle = {Proceedings of the {{Second Ukrainian Natural Language Processing Workshop}} ({{UNLP}})},
  author = {Solopova, Veronika and Benzm{\"u}ller, Christoph and Landgraf, Tim},
  editor = {Romanyshyn, Mariana},
  year = 2023,
  month = may,
  pages = {40--48},
  publisher = {Association for Computational Linguistics},
  address = {Dubrovnik, Croatia},
  doi = {10.18653/v1/2023.unlp-1.5},
  urldate = {2024-10-16},
  abstract = {In the Russo-Ukrainian war, propaganda is produced by Russian state-run news outlets for both international and domestic audiences. Its content and form evolve and change with time as the war continues. This constitutes a challenge to content moderation tools based on machine learning when the data used for training and the current news start to differ significantly. In this follow-up study, we evaluate our previous BERT and SVM models that classify Pro-Kremlin propaganda from a Pro-Western stance, trained on the data from news articles and telegram posts at the start of 2022, on the new 2023 subset. We examine both classifiers' errors and perform a comparative analysis of these subsets to investigate which changes in narratives provoke drops in performance.},
  annotation = {Read\_Status: Read\\
Read\_Status\_Date: 2025-07-03T14:43:20.505Z},
  file = {C\:\\Users\\nasty\\Documents\\Zotero\\Solopova et al. - 2023 - The Evolution of Pro-Kremlin Propaganda From a Machine Learning and Linguistics Perspective 1.pdf;C\:\\Users\\nasty\\Documents\\Zotero\\Solopova et al. - 2023 - The Evolution of Pro-Kremlin Propaganda From a Machine Learning and Linguistics Perspective 1.pdf;C\:\\Users\\nasty\\Documents\\Zotero\\Solopova et al. - 2023 - The Evolution of Pro-Kremlin Propaganda From a Machine Learning and Linguistics Perspective 1.pdf}
}

@article{soniAbolitionistNetworksModeling2021,
  title = {Abolitionist {{Networks}}: {{Modeling Language Change}} in {{Nineteenth-Century Activist Newspapers}}},
  shorttitle = {Abolitionist {{Networks}}},
  author = {Soni, Sandeep and Klein, Lauren F. and Eisenstein, Jacob},
  year = 2021,
  month = jan,
  journal = {Journal of Cultural Analytics},
  volume = {6},
  number = {1},
  doi = {10.22148/001c.18841},
  urldate = {2025-07-01},
  abstract = {The abolitionist movement of the nineteenth-century United States remains among the most significant social and political movements in US history. Abolitionist newspapers played a crucial role in spreading information and shaping public opinion around a range of issues relating to the abolition of slavery. These newspapers also serve as a primary source of information about the movement for scholars today, resulting in powerful new accounts of the movement and its leaders. This paper supplements recent qualitative work on the role of women in abolition's vanguard, as well as the role of the Black press, with a quantitative text modeling approach. Using diachronic word embeddings, we identify which newspapers tended to lead lexical semantic innovations --- the introduction of new usages of specific words --- and which newspapers tended to follow. We then aggregate the evidence across hundreds of changes into a weighted network with the newspapers as nodes; directed edge weights represent the frequency with which each newspaper led the other in the adoption of a lexical semantic change. Analysis of this network reveals pathways of lexical semantic influence, distinguishing leaders from followers, as well as others who stood apart from the semantic changes that swept through this period. More specifically, we find that two newspapers edited by women -- The Provincial Freeman and The Lily -- led a large number of semantic changes in our corpus, lending additional credence to the argument that a multiracial coalition of women led the abolitionist movement in terms of both thought and action. It also contributes additional complexity to the scholarship that has sought to tease apart the relation of the abolitionist movement to the women's suffrage movement, and the vexed racial politics that characterized their relation.},
  langid = {english},
  keywords = {icwsm2025},
  annotation = {Read\_Status: To Read\\
Read\_Status\_Date: 2025-07-03T15:09:47.779Z},
  file = {C:\Users\nasty\Documents\Zotero\Soni et al. - 2021 - Abolitionist Networks Modeling Language Change in Nineteenth-Century Activist Newspapers.pdf}
}

@inproceedings{steuerModelingDiachronicChange2024,
  title = {Modeling {{Diachronic Change}} in {{English Scientific Writing}} over 300+ {{Years}} with {{Transformer-based Language Model Surprisal}}},
  booktitle = {Proceedings of the 17th {{Workshop}} on {{Building}} and {{Using Comparable Corpora}} ({{BUCC}}) @ {{LREC-COLING}} 2024},
  author = {Steuer, Julius and Krielke, Marie-Pauline and Fischer, Stefan and {Degaetano-Ortlieb}, Stefania and Mosbach, Marius and Klakow, Dietrich},
  editor = {Zweigenbaum, Pierre and Rapp, Reinhard and Sharoff, Serge},
  year = 2024,
  month = may,
  pages = {12--23},
  publisher = {{ELRA and ICCL}},
  address = {Torino, Italia},
  urldate = {2024-12-16},
  annotation = {Read\_Status: To Read\\
Read\_Status\_Date: 2025-07-28T15:14:48.005Z},
  file = {C:\Users\nasty\Documents\Zotero\Steuer et al. - 2024 - Modeling Diachronic Change in English Scientific Writing over 300+ Years with Transformer-based Lang 1.pdf}
}

@inproceedings{tahmasebiImprovingGeneralizationMultimodal2023,
  title = {Improving {{Generalization}} for {{Multimodal Fake News Detection}}},
  booktitle = {Proceedings of the 2023 {{ACM International Conference}} on {{Multimedia Retrieval}}},
  author = {Tahmasebi, Sahar and Hakimov, Sherzod and Ewerth, Ralph and {M{\"u}ller-Budack}, Eric},
  year = 2023,
  month = jun,
  series = {{{ICMR}} '23},
  pages = {581--585},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  doi = {10.1145/3591106.3592230},
  urldate = {2025-09-23},
  abstract = {The increasing proliferation of misinformation and its alarming impact have motivated both industry and academia to develop approaches for fake news detection. However, state-of-the-art approaches are usually trained on datasets of smaller size or with a limited set of specific topics. As a consequence, these models lack generalization capabilities and are not applicable to real-world data. In this paper, we propose three models that adopt and fine-tune state-of-the-art multimodal transformers for multimodal fake news detection. We conduct an in-depth analysis by manipulating the input data aimed to explore models performance in realistic use cases on social media. Our study across multiple models demonstrates that these systems suffer significant performance drops against manipulated data. To reduce the bias and improve model generalization, we suggest training data augmentation to conduct more meaningful experiments for fake news detection on social media. The proposed data augmentation techniques enable models to generalize better and yield improved state-of-the-art results.},
  isbn = {979-8-4007-0178-8},
  keywords = {dh_summer_school},
  annotation = {Read\_Status: Not Reading\\
Read\_Status\_Date: 2025-09-25T18:27:22.996Z},
  file = {C:\Users\nasty\Documents\Zotero\Tahmasebi et al. - 2023 - Improving Generalization for Multimodal Fake News Detection.pdf}
}

@incollection{tahmasebiSurveyComputationalApproaches2021,
  title = {Survey of Computational Approaches to Lexical Semantic Change Detection},
  author = {Tahmasebi, Nina and Borin, Lars and Jatowt, Adam},
  year = 2021,
  month = jun,
  publisher = {Zenodo},
  doi = {10.5281/ZENODO.5040302},
  urldate = {2024-11-07},
  abstract = {Our languages are in constant flux driven by external factors such as cultural, societal and technological changes, as well as by only partially understood internal motivations. Words acquire new meanings and lose old senses, new words are coined or borrowed from other languages and obsolete words slide into obscurity. Understanding the characteristics of shifts in the meaning and in the use of words is useful for those who work with the content of historical texts, the interested general public, but also in and of itself. The findings from automatic lexical semantic change detection and the models of diachronic conceptual change are also currently being incorporated in approaches for measuring document across-time similarity, information retrieval from long term document archives, the design of OCR algorithms, and so on. In recent years we have seen a surge in interest in the academic community in computational methods and tools supporting inquiry into diachronic conceptual change and lexical replacement. This article provides a comprehensive survey of recent computational techniques to tackle both.},
  copyright = {Creative Commons Attribution 4.0 International, Open Access},
  isbn = {978-3-96110-312-6},
  langid = {english},
  annotation = {Read\_Status: To Read\\
Read\_Status\_Date: 2025-07-28T15:16:08.783Z},
  file = {C\:\\Users\\nasty\\Documents\\Zotero\\Tahmasebi et al. - 2021 - Survey of computational approaches to lexical semantic change detection 2.pdf;C\:\\Users\\User\\OneDrive - Universität des Saarlandes\\Documents\\Zotero\\Tahmasebi et al. - 2021 - Survey of computational approaches to lexical semantic change detection.pdf}
}

@article{tamrazyanMEDIAREPRESENTATIONSUKRAINE2023,
  title = {{{MEDIA REPRESENTATIONS OF UKRAINE AND UKRAINIANS}}: {{A CORPUS-BASED ANALYSIS OF AMERICAN AND BRITISH MEDIA SOURCES}}},
  shorttitle = {{{MEDIA REPRESENTATIONS OF UKRAINE AND UKRAINIANS}}},
  author = {Tamrazyan, Hamest S. and Starovoitova, Anastasiia {\cyrchar\CYRA}.},
  year = 2023,
  month = jun,
  journal = {Scientific Journal of Mykhailo Dragomanov State University of Ukraine. Series 9. Current Trends in Language Development},
  number = {25},
  pages = {90--101},
  issn = {2523-4242},
  doi = {10.31392/NPU-nc.series9.2023.25.07},
  urldate = {2025-10-29},
  abstract = {This article presents a corpus-based analysis of media representations of Ukraine and Ukrainians in popular American and British media sources. The study aims to examine language usage, framing techniques, and the frequency of specific words and phrases to shed light on the construction and dissemination of narratives. Two media sub corpora, the British media sub corpus and the American media sub corpus, were compiled using the \#Lancsbox interface and search tools like Google. The corpora consisted of 100 articles each, covering the period from late 2013 to April 2022. The analysis revealed several key findings. First, these findings underscore the media's significant role in shaping public perception and understanding of Ukraine and Ukrainians. Secondly, they highlight the importance of accurate language usage, framing techniques, and cultural sensitivity in media representations. Third, the study's conclusions have practical implications for promoting more accurate and respectful media portrayals of Ukraine and Ukrainians. Further research avenues include exploring a broader range of media sources, investigating the influence of geopolitical factors on media representations, and examining the impact of media discourse on public attitudes and perceptions.},
  langid = {american},
  keywords = {corpus-based analysis,framing techniques,language usage.,media representations,Ukraine,Ukrainians},
  annotation = {Read\_Status: New\\
Read\_Status\_Date: 2025-10-29T17:38:22.352Z},
  file = {C:\Users\nasty\Documents\Zotero\Tamrazyan and Starovoitova - 2023 - MEDIA REPRESENTATIONS OF UKRAINE AND UKRAINIANS A CORPUS-BASED ANALYSIS OF AMERICAN AND BRITISH MED.pdf}
}

@article{teichLessMoreMore2021,
  title = {Less Is {{More}}/{{More Diverse}}: {{On The Communicative Utility}} of {{Linguistic Conventionalization}}},
  shorttitle = {Less Is {{More}}/{{More Diverse}}},
  author = {Teich, Elke and Fankhauser, Peter and {Degaetano-Ortlieb}, Stefania and Bizzoni, Yuri},
  year = 2021,
  publisher = {Saarl\"andische Universit\"ats- und Landesbibliothek},
  issn = {2297-900X},
  doi = {10.22028/D291-38068},
  urldate = {2024-12-14},
  abstract = {We present empirical evidence of the communicative utility of conventionalization, i.e., convergence in linguistic usage over time, and diversification, i.e., linguistic items acquiring different, more specific usages/meanings. From a diachronic perspective, conventionalization plays a crucial role in language change as a condition for innovation and grammaticalization (Bybee, 2010; Schmid, 2015) and diversification is a cornerstone in the formation of sublanguages/registers, i.e., functional linguistic varieties (Halliday, 1988; Harris, 1991). While it is widely acknowledged that change in language use is primarily socio-culturally determined pushing towards greater linguistic expressivity, we here highlight the limiting function of communicative factors on diachronic linguistic variation showing that conventionalization and diversification are associated with a reduction of linguistic variability. To be able to observe effects of linguistic variability reduction, we first need a well-defined notion of choice in context. Linguistically, this implies the paradigmatic axis of linguistic organization, i.e., the sets of linguistic options available in a given or similar syntagmatic contexts. Here, we draw on word embeddings, weakly neural distributional language models that have recently been employed to model lexical-semantic change and allow us to approximate the notion of paradigm by neighbourhood in vector space. Second, we need to capture changes in paradigmatic variability, i.e. reduction/expansion of linguistic options in a given context. As a formal index of paradigmatic variability we use entropy, which measures the contribution of linguistic units (e.g., words) in predicting linguistic choice in bits of information. Using entropy provides us with a link to a communicative interpretation, as it is a well-established measure of communicative efficiency with implications for cognitive processing (Linzen and Jaeger, 2016; Venhuizen et al., 2019); also, entropy is negatively correlated with distance in (word embedding) spaces which in turn shows cognitive reflexes in certain language processing tasks (Mitchel et al., 2008; Auguste et al., 2017). In terms of domain we focus on science, looking at the diachronic development of scientific English from the 17th century to modern time. This provides us with a fairly constrained yet dynamic domain of discourse that has witnessed a powerful systematization throughout the centuries and developed specific linguistic conventions geared towards efficient communication. Overall, our study confirms the assumed trends of conventionalization and diversification shown by diachronically decreasing entropy, interspersed with local, temporary entropy highs pointing to phases of linguistic expansion pertaining primarily to introduction of new technical terminology.},
  copyright = {Namensnennung 3.0 Deutschland},
  langid = {english},
  annotation = {Accepted: 2022-11-18T09:19:13Z\\
Read\_Status: Read\\
Read\_Status\_Date: 2025-07-28T15:16:56.710Z},
  file = {C\:\\Users\\nasty\\Documents\\Zotero\\Teich et al. - 2021 - Less is MoreMore Diverse On The Communicative Utility of Linguistic Conventionalization 1.pdf;C\:\\Users\\nasty\\Documents\\Zotero\\Teich et al. - 2021 - Less is MoreMore Diverse On The Communicative Utility of Linguistic Conventionalization.pdf}
}

@misc{tongletImageTellMe2024,
  title = {"{{Image}}, {{Tell}} Me Your Story!" {{Predicting}} the Original Meta-Context of Visual Misinformation},
  author = {Tonglet, Jonathan and Moens, Marie-Francine and Gurevych, Iryna},
  year = 2024,
  month = aug,
  number = {arXiv:2408.09939},
  eprint = {2408.09939},
  primaryclass = {cs},
  publisher = {arXiv},
  doi = {10.48550/arXiv.2408.09939},
  urldate = {2024-12-18},
  abstract = {To assist human fact-checkers, researchers have developed automated approaches for visual misinformation detection. These methods assign veracity scores by identifying inconsistencies between the image and its caption, or by detecting forgeries in the image. However, they neglect a crucial point of the human fact-checking process: identifying the original meta-context of the image. By explaining what is actually true about the image, fact-checkers can better detect misinformation, focus their efforts on check-worthy visual content, engage in counter-messaging before misinformation spreads widely, and make their explanation more convincing. Here, we fill this gap by introducing the task of automated image contextualization. We create 5Pils, a dataset of 1,676 fact-checked images with question-answer pairs about their original meta-context. Annotations are based on the 5 Pillars fact-checking framework. We implement a first baseline that grounds the image in its original meta-context using the content of the image and textual evidence retrieved from the open web. Our experiments show promising results while highlighting several open challenges in retrieval and reasoning. We make our code and data publicly available.},
  archiveprefix = {arXiv},
  keywords = {Computer Science - Computation and Language},
  annotation = {Read\_Status: Not Reading\\
Read\_Status\_Date: 2025-07-28T15:18:19.520Z},
  file = {C\:\\Users\\nasty\\Documents\\Zotero\\Tonglet et al. - 2024 - Image, Tell me your story! Predicting the original meta-context of visual misinformation 1.pdf;C\:\\Users\\nasty\\Zotero\\storage\\6AY59VG7\\2408.html;C\:\\Users\\nasty\\Zotero\\storage\\NKTCUSNI\\2408.html}
}

@incollection{toolanNarrativeLinguisticStructural2006,
  title = {Narrative: {{Linguistic}} and {{Structural Theories}}},
  shorttitle = {Narrative},
  booktitle = {Encyclopedia of {{Language}} \& {{Linguistics}} ({{Second Edition}})},
  author = {Toolan, M.},
  editor = {Brown, Keith},
  year = 2006,
  month = jan,
  pages = {459--473},
  publisher = {Elsevier},
  address = {Oxford},
  doi = {10.1016/B0-08-044854-2/00528-9},
  urldate = {2025-08-05},
  abstract = {This article surveys some of the most influential theory and commentary in the Western linguistic tradition on narrative as a genre or interactional activity. The fundamental domains of story and discourse, the structure and grammar of plot, and the complex narrative manipulations of temporal order and pace that characterize sophisticated literary narratives are all considered. The distinctive feature approach to character and effects of naming and of the dramatizing of setting are also outlined, before a fuller treatment is given of contrasting styles of narration (including narratorial unreliability) and of point of view (focalization).},
  isbn = {978-0-08-044854-1},
  keywords = {character,duration,event,mode of narration,narrative_theory,teleology,temporal order,viewpoint,voice},
  annotation = {Read\_Status: To Read\\
Read\_Status\_Date: 2025-08-20T13:55:55.119Z},
  file = {C\:\\Users\\nasty\\Documents\\Zotero\\Toolan - 2006 - Narrative Linguistic and Structural Theories.pdf;C\:\\Users\\nasty\\Zotero\\storage\\279AUPSU\\B0080448542005289.html}
}

@misc{traugottSemanticChange2005,
  title = {Semantic Change},
  author = {Traugott, Elizabeth C.},
  year = 2005,
  urldate = {2025-08-05},
  howpublished = {https://philpapers.org/rec/TRASC},
  langid = {english},
  annotation = {Read\_Status: To Read\\
Read\_Status\_Date: 2025-08-25T14:40:19.415Z},
  file = {C:\Users\nasty\Documents\Zotero\Traugott - 2005 - Semantic change 2.pdf}
}

@incollection{traugottSemanticChangeBleaching2006,
  title = {Semantic {{Change}}: {{Bleaching}}, {{Strengthening}}, {{Narrowing}}, {{Extension}}},
  shorttitle = {Semantic {{Change}}},
  booktitle = {Encyclopedia of {{Language}} \& {{Linguistics}} ({{Second Edition}})},
  author = {Traugott, E. C.},
  editor = {Brown, Keith},
  year = 2006,
  month = jan,
  pages = {124--131},
  publisher = {Elsevier},
  address = {Oxford},
  doi = {10.1016/B0-08-044854-2/01105-6},
  urldate = {2025-08-05},
  abstract = {The standard six types of semantic change (broadening, narrowing, amelioration, pejoration, metaphor, and metonymy) are introduced and discussed further in terms of how they have been construed from the perspectives of semantic fields and Cognitive Linguistics (including Prototype Theory), Neo-Gricean pragmatics (including Invited Inferencing Theory), and Grammaticalization Theory. Bleaching and subjectification, although usually associated with grammaticalization, are shown to be general types of semantic change, not unique to grammaticalization. Three constraints on semantic change are outlined: avoidance of Euclidean space in grammaticalization, avoidance of synonymy, and avoidance of homonymy.},
  isbn = {978-0-08-044854-1},
  keywords = {amelioration,autohyponymy,bleaching,bridging context,broadening,dysphemism,enantiosemy,euphemism,generalization,homonymiphobia,metaphorization,metonymization,narrowing,pejoration,subjectification},
  annotation = {Read\_Status: To Read\\
Read\_Status\_Date: 2025-08-20T13:56:46.265Z},
  file = {C\:\\Users\\nasty\\Documents\\Zotero\\Traugott - 2006 - Semantic Change Bleaching, Strengthening, Narrowing, Extension.pdf;C\:\\Users\\nasty\\Zotero\\storage\\XQTMXGGZ\\B0080448542011056.html}
}

@inproceedings{tripodiTracingAntisemiticLanguage2019,
  title = {Tracing {{Antisemitic Language Through Diachronic Embedding Projections}}: {{France}} 1789-1914},
  shorttitle = {Tracing {{Antisemitic Language Through Diachronic Embedding Projections}}},
  booktitle = {Proceedings of the 1st {{International Workshop}} on {{Computational Approaches}} to {{Historical Language Change}}},
  author = {Tripodi, Rocco and Warglien, Massimo and Levis Sullam, Simon and Paci, Deborah},
  editor = {Tahmasebi, Nina and Borin, Lars and Jatowt, Adam and Xu, Yang},
  year = 2019,
  month = aug,
  pages = {115--125},
  publisher = {Association for Computational Linguistics},
  address = {Florence, Italy},
  doi = {10.18653/v1/W19-4715},
  urldate = {2024-11-18},
  abstract = {We investigate some aspects of the history of antisemitism in France, one of the cradles of modern antisemitism, using diachronic word embeddings. We constructed a large corpus of French books and periodicals issues that contain a keyword related to Jews and performed a diachronic word embedding over the 1789-1914 period. We studied the changes over time in the semantic spaces of 4 target words and performed embedding projections over 6 streams of antisemitic discourse. This allowed us to track the evolution of antisemitic bias in the religious, economic, socio-politic, racial, ethic and conspiratorial domains. Projections show a trend of growing antisemitism, especially in the years starting in the mid-80s and culminating in the Dreyfus affair. Our analysis also allows us to highlight the peculiar adverse bias towards Judaism in the broader context of other religions.},
  annotation = {Read\_Status: Read\\
Read\_Status\_Date: 2025-07-28T15:19:29.416Z},
  file = {C:\Users\nasty\Documents\Zotero\Tripodi et al. - 2019 - Tracing Antisemitic Language Through Diachronic Embedding Projections France 1789-1914 1.pdf}
}

@article{trokhymovychCharacterizingKnowledgeManipulation2025,
  title = {Characterizing {{Knowledge Manipulation}} in a {{Russian Wikipedia Fork}}},
  author = {Trokhymovych, Mykola and Kosovan, Oleksandr and Forrester, Nathan and Arag{\'o}n, Pablo and {Saez-Trumper}, Diego and {Baeza-Yates}, Ricardo},
  year = 2025,
  month = jun,
  journal = {Proceedings of the International AAAI Conference on Web and Social Media},
  volume = {19},
  pages = {1924--1936},
  issn = {2334-0770},
  doi = {10.1609/icwsm.v19i1.35910},
  urldate = {2025-10-27},
  abstract = {Wikipedia is powered by MediaWiki, a free and open-source software that is also the infrastructure for many other wiki-based online encyclopedias. These include the recently launched website Ruwiki, which has copied and modified the original Russian Wikipedia content to conform to Russian law. To identify practices and narratives that could be associated with different forms of knowledge manipulation, this article presents an in-depth analysis of this Russian Wikipedia fork. We propose a methodology to characterize the main changes with respect to the original version. The foundation of this study is a comprehensive comparative analysis of more than 1.9M articles from Russian Wikipedia and its fork. Using meta-information and geographical, temporal, categorical, and textual features, we explore the changes made by Ruwiki editors. Furthermore, we present a classification of the main topics of knowledge manipulation in this fork, including a numerical estimation of their scope. This research not only sheds light on significant changes within Ruwiki, but also provides a methodology that could be applied to analyze other Wikipedia forks and similar collaborative projects.},
  copyright = {Copyright (c) 2025 Association for the Advancement of Artificial Intelligence},
  langid = {english},
  keywords = {icwsm2025},
  annotation = {Read\_Status: Read\\
Read\_Status\_Date: 2025-10-27T19:57:11.493Z},
  file = {C:\Users\nasty\Documents\Zotero\Trokhymovych et al. - 2025 - Characterizing Knowledge Manipulation in a Russian Wikipedia Fork.pdf}
}

@misc{tsengFakeNarrativesFirstForays2023,
  title = {{{FakeNarratives}} -- {{First Forays}} in {{Understanding Narratives}} of {{Disinformation}} in {{Public}} and {{Alternative News Videos}}},
  author = {Tseng, Chiao-I and Liebl, Bernhard and Burghardt, Manuel and Bateman, John},
  year = 2023,
  month = mar,
  publisher = {Zenodo},
  doi = {10.5281/zenodo.7715277},
  urldate = {2025-09-23},
  abstract = {In this paper we present first results from an ongoing BMBF project on FakeNarratives. In the project we investigate a socially significant class of audiovisual artifacts: TV news. We address the research hypothesis that news of this kind increasingly employs strategies of audiovisual narrative that may undercut, undermine or construct ideological positions and evaluations of news content independently of what may be simply stated, for example, in accompanying spoken text. We present first results from a comparison of Bild TV and Tagesschau and also introduce a first prototype vor the visualization and analysis of news videos." Ein Beitrag zur 9. Tagung des Verbands "Digital Humanities im deutschsprachigen Raum" - DHd 2023 Open Humanities Open Culture.},
  keywords = {Annotieren,Beziehungsanalyse,dh_summer_school,DHd2023,disinformation,Inhaltsanalyse,Multimodale Kommunikation,narrative strategies,Video,video analytics,Visualisierung},
  annotation = {Read\_Status: To Read\\
Read\_Status\_Date: 2025-09-25T18:28:37.064Z},
  file = {C:\Users\nasty\Documents\Zotero\Tseng et al. - 2023 - FakeNarratives – First Forays in Understanding Narratives of Disinformation in Public and Alternativ.pdf}
}

@inproceedings{ustyianovychInstantMessagingPlatforms2024,
  title = {Instant {{Messaging Platforms News Multi-Task Classification}} for {{Stance}}, {{Sentiment}}, and {{Discrimination Detection}}},
  booktitle = {Proceedings of the {{Third Ukrainian Natural Language Processing Workshop}} ({{UNLP}}) @ {{LREC-COLING}} 2024},
  author = {Ustyianovych, Taras and Barbosa, Denilson},
  editor = {Romanyshyn, Mariana and Romanyshyn, Nataliia and Hlybovets, Andrii and Ignatenko, Oleksii},
  year = 2024,
  month = may,
  pages = {30--40},
  publisher = {{ELRA and ICCL}},
  address = {Torino, Italia},
  urldate = {2024-10-16},
  abstract = {In the digital age, geopolitical events frequently catalyze discussions among global web users. Platforms such as social networks and messaging applications serve as vital means for information spreading and acquisition. The Russian aggression against Ukraine has notably intensified online discourse on the matter, drawing a significant audience eager for real-time updates. This surge in online activity inevitably results in the proliferation of content, some of which may be unreliable or manipulative. Given this context, the identification of such content with information distortion is imperative to mitigate bias and promote fairness. However, this task presents considerable challenges, primarily due to the lack of sophisticated language models capable of understanding the nuances and context of texts in low-resource languages, and the scarcity of well-annotated datasets for training such models. To address these gaps, we introduce the TRWU dataset - a meticulously annotated collection of Telegram news about the Russian war in Ukraine gathered starting from January 1, 2022. This paper outlines our methodology for semantic analysis and classification of these messages, aiming to ascertain their bias. Such an approach enhances our ability to detect manipulative and destructive content. Through descriptive statistical analysis, we explore deviations in message sentiment, stance, and metadata across different types of channels and levels of content creation activity. Our findings indicate a predominance of negative sentiment within the dataset. Additionally, our research elucidates distinct differences in the linguistic choices and phraseology among channels, based on their stance towards the war. This study contributes to the broader effort of understanding the spread and mitigating the impact of biased and manipulative content in digital communications.},
  annotation = {Read\_Status: Read\\
Read\_Status\_Date: 2025-07-03T14:48:15.543Z},
  file = {C\:\\Users\\nasty\\Documents\\Zotero\\Ustyianovych and Barbosa - 2024 - Instant Messaging Platforms News Multi-Task Classification for Stance, Sentiment, and Discrimination 1.pdf;C\:\\Users\\nasty\\Documents\\Zotero\\Ustyianovych and Barbosa - 2024 - Instant Messaging Platforms News Multi-Task Classification for Stance, Sentiment, and Discrimination 1.pdf;C\:\\Users\\nasty\\Documents\\Zotero\\Ustyianovych and Barbosa - 2024 - Instant Messaging Platforms News Multi-Task Classification for Stance, Sentiment, and Discrimination 1.pdf}
}

@inproceedings{vanetikPropagandaDetectionRussian2023,
  title = {Propaganda {{Detection}} in {{Russian Telegram Posts}} in the {{Scope}} of the {{Russian Invasion}} of {{Ukraine}}},
  booktitle = {Proceedings of the {{Conference Recent Advances}} in {{Natural Language Processing}} - {{Large Language Models}} for {{Natural Language Processings}}},
  author = {Vanetik, Natalia and Litvak, Marina and Reviakin, Egor and Tyamanova, Margarita},
  year = 2023,
  pages = {1162--1170},
  publisher = {INCOMA Ltd., Shoumen, BULGARIA},
  doi = {10.26615/978-954-452-092-2_123},
  urldate = {2024-12-11},
  abstract = {The emergence of social media has made it more difficult to recognize and analyze misinformation efforts. Popular messaging software Telegram (Durov, 2013) has developed into a medium for disseminating political messages and misinformation, particularly in light of the conflict in Ukraine (Wikipedia contributors, 2023). In this paper, we introduce a sizable corpus of Telegram posts containing proRussian propaganda and benign political texts. We evaluate the corpus by applying natural language processing (NLP) techniques to the task of text classification in this corpus. Our findings indicate that, with an overall accuracy of over 96\% for confirmed sources as propagandists and oppositions and 92\% for unconfirmed sources, our method can successfully identify and categorize pro-Russian propaganda posts. We highlight the consequences of our research for comprehending political communications and propaganda on social media.},
  isbn = {978-954-452-092-2},
  langid = {english},
  annotation = {Read\_Status: Read\\
Read\_Status\_Date: 2025-10-27T15:47:03.502Z},
  file = {C\:\\Users\\nasty\\Documents\\Zotero\\Vanetik et al. - 2023 - Propaganda Detection in Russian Telegram Posts in the Scope of the Russian Invasion of Ukraine 2.pdf;C\:\\Users\\nasty\\Documents\\Zotero\\Vanetik et al. - 2023 - Propaganda Detection in Russian Telegram Posts in the Scope of the Russian Invasion of Ukraine.pdf}
}

@inproceedings{vargasImprovingExplainableFactChecking2024,
  title = {Improving {{Explainable Fact-Checking}} via {{Sentence-Level Factual Reasoning}}},
  booktitle = {Proceedings of the {{Seventh Fact Extraction}} and {{VERification Workshop}} ({{FEVER}})},
  author = {Vargas, Francielle and Salles, Isadora and Alves, Diego and Agrawal, Ameeta and Pardo, Thiago A. S. and Benevenuto, Fabr{\'i}cio},
  year = 2024,
  pages = {192--204},
  publisher = {Association for Computational Linguistics},
  address = {Miami, Florida, USA},
  doi = {10.18653/v1/2024.fever-1.23},
  urldate = {2024-12-10},
  abstract = {Most existing fact-checking systems are unable to explain their decisions by providing relevant rationales (justifications) for their predictions. It highlights a lack of transparency that poses significant risks, such as the prevalence of unexpected biases, which may increase political polarization due to limitations in impartiality. To address this critical gap, we introduce SentenceLevel Factual Reasoning (SELFAR)1, aimed at improving explainable fact-checking. SELFAR relies on fact extraction and verification by predicting the news source reliability and factuality (veracity) of news articles or claims at the sentence level, generating post-hoc explanations using SHAP/LIME and zero-shot prompts. Our experiments show that unreliable news stories predominantly consist of subjective statements, in contrast to reliable ones. Consequently, predicting unreliable news articles at the sentence level by analyzing impartiality and subjectivity is a promising approach for fact extraction and improving explainable fact-checking. Furthermore, LIME outperforms SHAP in explaining predictions on reliability. Additionally, while zero-shot prompts provide highly readable explanations and achieve an accuracy of 0.71 in predicting factuality, their tendency to hallucinate remains a challenge. Lastly, this paper also presents the first study on explainable factchecking in the Portuguese language.},
  langid = {english},
  annotation = {Read\_Status: To Read\\
Read\_Status\_Date: 2025-08-27T13:39:19.507Z},
  file = {C:\Users\nasty\Documents\Zotero\Vargas et al. - 2024 - Improving Explainable Fact-Checking via Sentence-Level Factual Reasoning 1.pdf}
}

@inproceedings{vestelDetectingDivergentLanguage2025,
  title = {Detecting Divergent Language Use in {{Russian Media}} during the {{Russo-Ukrainian War}}: {{Steps}} towards Interpretable Propaganda Detection and Analysis},
  author = {Vestel, Anastasiia and {Degaetano-Ortlieb}, Stefania},
  year = 2025,
  address = {Lisbon},
  eventtitle = {Digital {{Humanities}} ({{DH2025}})},
  annotation = {Read\_Status: Wrote\\
Read\_Status\_Date: 2025-08-25T15:14:52.388Z}
}

@inproceedings{vestelInterpretableModelsDetecting2025,
  title = {Interpretable {{Models}} for {{Detecting Linguistic Variation}} in {{Russian Media}}: {{Towards Unveiling Propagandistic Strategies}} during the {{Russo-Ukrainian War}}},
  shorttitle = {Interpretable {{Models}} for {{Detecting Linguistic Variation}} in {{Russian Media}}},
  booktitle = {Proceedings of the 9th {{Joint SIGHUM Workshop}} on {{Computational Linguistics}} for {{Cultural Heritage}}, {{Social Sciences}}, {{Humanities}} and {{Literature}} ({{LaTeCH-CLfL}} 2025)},
  author = {Vestel, Anastasiia and {Degaetano-Ortlieb}, Stefania},
  editor = {Kazantseva, Anna and Szpakowicz, Stan and {Degaetano-Ortlieb}, Stefania and Bizzoni, Yuri and Pagel, Janis},
  year = 2025,
  month = may,
  pages = {109--116},
  publisher = {Association for Computational Linguistics},
  address = {Albuquerque, New Mexico},
  doi = {10.18653/v1/2025.latechclfl-1.11},
  urldate = {2025-08-21},
  abstract = {With the start of the full-scale Russian invasion of Ukraine in February 2022, the spread of pro-Kremlin propaganda increased to justify the war, both in the official state media and social media. This position paper explores the theoretical background of propaganda detection in the given context and proposes a thorough methodology to investigate how language has been strategically manipulated to align with ideological goals and adapt to the changing narrative surrounding the invasion. Using the WarMM-2022 corpus, the study seeks to identify linguistic patterns across media types and their evolution over time. By doing so, we aim to enhance the understanding of the role of linguistic strategies in shaping propaganda narratives. The findings are intended to contribute to the broader discussion of information manipulation in politically sensitive contexts.},
  isbn = {979-8-89176-241-1},
  annotation = {Read\_Status: Wrote\\
Read\_Status\_Date: 2025-08-25T15:14:55.911Z},
  file = {C:\Users\nasty\Documents\Zotero\Vestel and Degaetano-Ortlieb - 2025 - Interpretable Models for Detecting Linguistic Variation in Russian Media Towards Unveiling Propagan.pdf}
}

@article{vestelWarSpecialMilitary2025,
  title = {From {{War}} to {{Special Military Operation}}: {{Interpretable Detection}} of {{Linguistic Propaganda Framing}} in {{Russian Media}}},
  author = {Vestel, Anastasiia and {Degaetano-Ortlieb}, Stefania},
  year = 2025,
  month = jun,
  journal = {Workshop Proceedings of the 19th International AAAI Conference on Web and Social Media},
  volume = {2025},
  pages = {50},
  doi = {10.36190/2025.50},
  urldate = {2025-09-03},
  langid = {american},
  annotation = {Read\_Status: Wrote\\
Read\_Status\_Date: 2025-09-04T09:17:04.722Z},
  file = {C:\Users\nasty\Documents\Zotero\Vestel and Degaetano-Ortlieb - 2025 - From War to Special Military Operation Interpretable Detection of Linguistic Propaganda Framing in 1.pdf}
}

@misc{vestelWordsWarHow2025,
  type = {Substack Newsletter},
  title = {Words of {{War}}: {{How Language Shapes Propaganda}}},
  shorttitle = {Words of {{War}}},
  author = {Vestel, Anastasiia},
  year = 2025,
  month = jun,
  journal = {Language \& Technology},
  urldate = {2025-07-24},
  abstract = {Insights from a study on linguistic variation of media types in the context of the Russo-Ukrainian war},
  annotation = {Read\_Status: Wrote\\
Read\_Status\_Date: 2025-08-25T15:14:43.256Z}
}

@inproceedings{vylomovaEvaluationSemanticChange2019,
  title = {Evaluation of {{Semantic Change}} of {{Harm-Related Concepts}} in {{Psychology}}},
  booktitle = {Proceedings of the 1st {{International Workshop}} on {{Computational Approaches}} to {{Historical Language Change}}},
  author = {Vylomova, Ekaterina and Murphy, Sean and Haslam, Nicholas},
  editor = {Tahmasebi, Nina and Borin, Lars and Jatowt, Adam and Xu, Yang},
  year = 2019,
  month = aug,
  pages = {29--34},
  publisher = {Association for Computational Linguistics},
  address = {Florence, Italy},
  doi = {10.18653/v1/W19-4704},
  urldate = {2024-11-18},
  abstract = {The paper focuses on diachronic evaluation of semantic changes of harm-related concepts in psychology. More specifically, we investigate a hypothesis that certain concepts such as ``addiction'', ``bullying'', ``harassment'', ``prejudice'', and ``trauma'' became broader during the last four decades. We evaluate semantic changes using two models: an LSA-based model from Sagi et al. (2009) and a diachronic adaptation of word2vec from Hamilton et al. (2016), that are trained on a large corpus of journal abstracts covering the period of 1980-- 2019. Several concepts showed evidence of broadening. ``Addiction'' moved from physiological dependency on a substance to include psychological dependency on gaming and the Internet. Similarly, ``harassment'' and ``trauma'' shifted towards more psychological meanings. On the other hand, ``bullying'' has transformed into a more victim-related concept and expanded to new areas such as workplaces.},
  annotation = {Read\_Status: To Read\\
Read\_Status\_Date: 2025-07-28T15:24:00.568Z},
  file = {C:\Users\nasty\Documents\Zotero\Vylomova et al. - 2019 - Evaluation of Semantic Change of Harm-Related Concepts in Psychology 1.pdf}
}

@inproceedings{wangFactcheckBenchFineGrainedEvaluation2024,
  title = {Factcheck-{{Bench}}: {{Fine-Grained Evaluation Benchmark}} for {{Automatic Fact-checkers}}},
  shorttitle = {Factcheck-{{Bench}}},
  booktitle = {Findings of the {{Association}} for {{Computational Linguistics}}: {{EMNLP}} 2024},
  author = {Wang, Yuxia and Gangi Reddy, Revanth and Mujahid, Zain Muhammad and Arora, Arnav and Rubashevskii, Aleksandr and Geng, Jiahui and Mohammed Afzal, Osama and Pan, Liangming and Borenstein, Nadav and Pillai, Aditya and Augenstein, Isabelle and Gurevych, Iryna and Nakov, Preslav},
  editor = {{Al-Onaizan}, Yaser and Bansal, Mohit and Chen, Yun-Nung},
  year = 2024,
  month = nov,
  pages = {14199--14230},
  publisher = {Association for Computational Linguistics},
  address = {Miami, Florida, USA},
  urldate = {2024-11-21},
  abstract = {The increased use of large language models (LLMs) across a variety of real-world applications calls for mechanisms to verify the factual accuracy of their outputs. In this work, we present Factcheck-Bench, a holistic end-to-end framework for annotating and evaluating the factuality of LLM-generated responses, which encompasses a multi-stage annotation scheme designed to yield detailed labels for fact-checking and correcting not just the final prediction, but also the intermediate steps that a fact-checking system might need to take. Based on this framework, we construct an open-domain factuality benchmark in three-levels of granularity: claim, sentence, and document. We further propose a system, Factcheck-GPT, which follows our framework, and we show that it outperforms several popular LLM fact-checkers. We make our annotation tool, annotated data, benchmark, and code available at https://github.com/yuxiaw/Factcheck-GPT.},
  annotation = {Read\_Status: Not Reading\\
Read\_Status\_Date: 2025-07-28T15:24:19.423Z},
  file = {C:\Users\nasty\Documents\Zotero\Wang et al. - 2024 - Factcheck-Bench Fine-Grained Evaluation Benchmark for Automatic Fact-checkers 1.pdf}
}

@inproceedings{wesselIntroducingMBIBFirst2023,
  title = {Introducing {{MBIB}} - {{The First Media Bias Identification Benchmark Task}} and {{Dataset Collection}}},
  booktitle = {Proceedings of the 46th {{International ACM SIGIR Conference}} on {{Research}} and {{Development}} in {{Information Retrieval}}},
  author = {Wessel, Martin and Horych, Tom{\'a}s and Ruas, Terry and Aizawa, Akiko and Gipp, Bela and Spinde, Timo},
  year = 2023,
  month = jul,
  series = {{{SIGIR}} '23},
  pages = {2765--2774},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  doi = {10.1145/3539618.3591882},
  urldate = {2025-03-26},
  abstract = {Although media bias detection is a complex multi-task problem, there is, to date, no unified benchmark grouping these evaluation tasks. We introduce the Media Bias Identification Benchmark (MBIB), a comprehensive benchmark that groups different types of media bias (e.g., linguistic, cognitive, political) under a common framework to test how prospective detection techniques generalize. After reviewing 115 datasets, we select nine tasks and carefully propose 22 associated datasets for evaluating media bias detection techniques. We evaluate MBIB using state-of-the-art Transformer techniques (e.g., T5, BART). Our results suggest that while hate speech, racial bias, and gender bias are easier to detect, models struggle to handle certain bias types, e.g., cognitive and political bias. However, our results show that no single technique can outperform all the others significantly.We also find an uneven distribution of research interest and resource allocation to the individual tasks in media bias. A unified benchmark encourages the development of more robust systems and shifts the current paradigm in media bias detection evaluation towards solutions that tackle not one but multiple media bias types simultaneously.},
  isbn = {978-1-4503-9408-6},
  annotation = {Read\_Status: Not Reading\\
Read\_Status\_Date: 2025-07-28T15:24:45.193Z}
}

@inproceedings{weversUsingWordEmbeddings2019,
  title = {Using {{Word Embeddings}} to {{Examine Gender Bias}} in {{Dutch Newspapers}}, 1950-1990},
  booktitle = {Proceedings of the 1st {{International Workshop}} on {{Computational Approaches}} to {{Historical Language Change}}},
  author = {Wevers, Melvin},
  editor = {Tahmasebi, Nina and Borin, Lars and Jatowt, Adam and Xu, Yang},
  year = 2019,
  month = aug,
  pages = {92--97},
  publisher = {Association for Computational Linguistics},
  address = {Florence, Italy},
  doi = {10.18653/v1/W19-4712},
  urldate = {2024-11-18},
  abstract = {Contemporary debates on filter bubbles and polarization in public and social media raise the question to what extent news media of the past exhibited biases. This paper specifically examines bias related to gender in six Dutch national newspapers between 1950 and 1990. We measure bias related to gender by comparing local changes in word embedding models trained on newspapers with divergent ideological backgrounds. We demonstrate clear differences in gender bias and changes within and between newspapers over time. In relation to themes such as sexuality and leisure, we see the bias moving toward women, whereas, generally, the bias shifts in the direction of men, despite growing female employment number and feminist movements. Even though Dutch society became less stratified ideologically (depillarization), we found an increasing divergence in gender bias between religious and social-democratic on the one hand and liberal newspapers on the other. Methodologically, this paper illustrates how word embeddings can be used to examine historical language change. Future work will investigate how fine-tuning deep contextualized embedding models, such as ELMO, might be used for similar tasks with greater contextual information.},
  annotation = {Read\_Status: Read\\
Read\_Status\_Date: 2025-07-28T15:25:00.570Z},
  file = {C\:\\Users\\nasty\\Documents\\Zotero\\Wevers - 2019 - Using Word Embeddings to Examine Gender Bias in Dutch Newspapers, 1950-1990 1.pdf;C\:\\Users\\nasty\\Documents\\Zotero\\Wevers - 2019 - Using Word Embeddings to Examine Gender Bias in Dutch Newspapers, 1950-1990.pdf}
}

@misc{wuRepresentingLongRangeContext2022,
  title = {Representing {{Long-Range Context}} for {{Graph Neural Networks}} with {{Global Attention}}},
  author = {Wu, Zhanghao and Jain, Paras and Wright, Matthew A. and Mirhoseini, Azalia and Gonzalez, Joseph E. and Stoica, Ion},
  year = 2022,
  month = jan,
  number = {arXiv:2201.08821},
  eprint = {2201.08821},
  primaryclass = {cs},
  publisher = {arXiv},
  doi = {10.48550/arXiv.2201.08821},
  urldate = {2024-12-17},
  abstract = {Graph neural networks are powerful architectures for structured datasets. However, current methods struggle to represent long-range dependencies. Scaling the depth or width of GNNs is insufficient to broaden receptive fields as larger GNNs encounter optimization instabilities such as vanishing gradients and representation oversmoothing, while pooling-based approaches have yet to become as universally useful as in computer vision. In this work, we propose the use of Transformer-based self-attention to learn long-range pairwise relationships, with a novel "readout" mechanism to obtain a global graph embedding. Inspired by recent computer vision results that find position-invariant attention performant in learning long-range relationships, our method, which we call GraphTrans, applies a permutation-invariant Transformer module after a standard GNN module. This simple architecture leads to state-of-the-art results on several graph classification tasks, outperforming methods that explicitly encode graph structure. Our results suggest that purely-learning-based approaches without graph structure may be suitable for learning high-level, long-range relationships on graphs. Code for GraphTrans is available at https://github.com/ucbrise/graphtrans.},
  archiveprefix = {arXiv},
  keywords = {Computer Science - Machine Learning},
  annotation = {Read\_Status: Not Reading\\
Read\_Status\_Date: 2025-07-28T15:25:59.746Z},
  file = {C:\Users\nasty\Documents\Zotero\Wu et al. - 2022 - Representing Long-Range Context for Graph Neural Networks with Global Attention 1.pdf}
}

@article{xiBlameGameUnderstanding2024,
  title = {The {{Blame Game}}: {{Understanding Blame Assignment}} in {{Social Media}}},
  shorttitle = {The {{Blame Game}}},
  author = {Xi, Ruijie and Singh, Munindar P.},
  year = 2024,
  month = apr,
  journal = {IEEE Transactions on Computational Social Systems},
  volume = {11},
  number = {2},
  pages = {2267--2276},
  issn = {2329-924X, 2373-7476},
  doi = {10.1109/TCSS.2023.3261242},
  urldate = {2025-07-01},
  abstract = {Cognitive and psychological studies on morality have proposed underlying linguistic and semantic factors. However, laboratory experiments in the philosophical literature often lack the nuances and complexity of real life. This paper examines how well the findings of these cognitive studies generalize to a corpus of over 30,000 narratives of tense social situations submitted to a popular social media forum. These narratives describe interpersonal moral situations or misgivings; other users judge from the post whether the author (protagonist ) or the opposing side (antagonist ) is morally culpable. Whereas previous work focuses on predicting the polarity of normative behaviors, we extend and apply natural language processing (NLP) techniques to understand the effects of descriptions of the people involved in these posts. We conduct extensive experiments to investigate the effect sizes of features to understand how they affect the assignment of blame on social media. Our findings show that aggregating psychology theories enables understanding real-life moral situations. Moreover, our results suggest that there exist biases in blame assignment on social media, such as males are more likely to receive blame no matter whether they are protagonists or antagonists.},
  copyright = {https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html},
  langid = {english},
  keywords = {icwsm2025},
  annotation = {Read\_Status: To Read\\
Read\_Status\_Date: 2025-07-03T15:10:28.825Z},
  file = {C:\Users\nasty\Documents\Zotero\Xi and Singh - 2024 - The Blame Game Understanding Blame Assignment in Social Media.pdf}
}

@article{xuEvolutionEmotionSemantics2021,
  title = {Evolution of Emotion Semantics},
  author = {Xu, Aotao and Stellar, Jennifer E. and Xu, Yang},
  year = 2021,
  month = dec,
  journal = {Cognition},
  volume = {217},
  pages = {104875},
  issn = {0010-0277},
  doi = {10.1016/j.cognition.2021.104875},
  urldate = {2024-11-05},
  abstract = {Humans possess the unique ability to communicate emotions through language. Although concepts like anger or awe are abstract, there is a shared consensus about what these English emotion words mean. This consensus may give the impression that their meaning is static, but we propose this is not the case. We cannot travel back to earlier periods to study emotion concepts directly, but we can examine text corpora, which have partially preserved the meaning of emotion words. Using natural language processing of historical text, we found evidence for semantic change in emotion words over the past century and that varying rates of change were predicted in part by an emotion concept's prototypicality---how representative it is of the broader category of ``emotion''. Prototypicality negatively correlated with historical rates of emotion semantic change obtained from text-based word embeddings, beyond more established variables including usage frequency in English and a second comparison language, French. This effect for prototypicality did not consistently extend to the semantic category of birds, suggesting its relevance for predicting semantic change may be category-dependent. Our results suggest emotion semantics are evolving over time, with prototypical emotion words remaining semantically stable, while other emotion words evolve more freely.},
  keywords = {Emotion,Prototype theory,Semantic change,Semantic stability,Word embedding},
  annotation = {Read\_Status: To Read\\
Read\_Status\_Date: 2025-07-28T15:27:13.271Z},
  file = {C\:\\Users\\nasty\\Documents\\Zotero\\Xu et al. - 2021 - Evolution of emotion semantics 2.pdf;C\:\\Users\\User\\OneDrive - Universität des Saarlandes\\Documents\\Zotero\\Xu et al. - 2021 - Evolution of emotion semantics.pdf}
}

@article{yaskovaRozvyazannyaZadachiKlasifikaciyi2023,
  title = {{\cyrchar\CYRR\cyrchar\cyro\cyrchar\cyrz\cyrchar\cyrv `\cyrchar\cyrya\cyrchar\cyrz\cyrchar\cyra\cyrchar\cyrn\cyrchar\cyrn\cyrchar\cyrya{} \cyrchar\cyrz\cyrchar\cyra\cyrchar\cyrd\cyrchar\cyra\cyrchar\cyrch\cyrchar\cyrii{} \cyrchar\cyrk\cyrchar\cyrl\cyrchar\cyra\cyrchar\cyrs\cyrchar\cyri\cyrchar\cyrf\cyrchar\cyrii\cyrchar\cyrk\cyrchar\cyra\cyrchar\cyrc\cyrchar\cyrii\cyrchar\cyryi{} \cyrchar\cyrt\cyrchar\cyre\cyrchar\cyrk\cyrchar\cyrs\cyrchar\cyrt\cyrchar\cyrii\cyrchar\cyrv{} \cyrchar\cyrm\cyrchar\cyre\cyrchar\cyrt\cyrchar\cyro\cyrchar\cyrd\cyrchar\cyra\cyrchar\cyrm\cyrchar\cyri{} \cyrchar\cyro\cyrchar\cyrb\cyrchar\cyrr\cyrchar\cyro\cyrchar\cyrb\cyrchar\cyrk\cyrchar\cyri{} \cyrchar\cyrp\cyrchar\cyrr\cyrchar\cyri\cyrchar\cyrr\cyrchar\cyro\cyrchar\cyrd\cyrchar\cyrn\cyrchar\cyro\cyrchar\cyryi{} \cyrchar\cyrm\cyrchar\cyro\cyrchar\cyrv\cyrchar\cyri}},
  author = {{\cyrchar\CYRYA}{\cyrchar\cyrs}{\cyrchar\cyrsftsn}{\cyrchar\cyrk}{\cyrchar\cyro}{\cyrchar\cyrv}{\cyrchar\cyra}, {\cyrchar\CYRD}{\cyrchar\cyra}{\cyrchar\cyrr}{\cyrchar\cyri}{\cyrchar\cyrn}{\cyrchar\cyra}},
  year = 2023,
  urldate = {2025-08-27},
  abstract = {\cyrchar\CYRU{} \cyrchar\cyrc\cyrchar\cyrii\cyrchar\cyrishrt{} \cyrchar\cyrk\cyrchar\cyrv\cyrchar\cyra\cyrchar\cyrl\cyrchar\cyrii\cyrchar\cyrf\cyrchar\cyrii\cyrchar\cyrk\cyrchar\cyra\cyrchar\cyrc\cyrchar\cyrii\cyrchar\cyrishrt\cyrchar\cyrn\cyrchar\cyrii\cyrchar\cyrishrt{} \cyrchar\cyrr\cyrchar\cyro\cyrchar\cyrb\cyrchar\cyro\cyrchar\cyrt\cyrchar\cyrii{} \cyrchar\cyrb\cyrchar\cyru\cyrchar\cyrd\cyrchar\cyru\cyrchar\cyrt\cyrchar\cyrsftsn{} \cyrchar\cyrd\cyrchar\cyro\cyrchar\cyrs\cyrchar\cyrl\cyrchar\cyrii\cyrchar\cyrd\cyrchar\cyrzh\cyrchar\cyre\cyrchar\cyrn\cyrchar\cyrii{} \cyrchar\cyrr\cyrchar\cyrii\cyrchar\cyrz\cyrchar\cyrn\cyrchar\cyro\cyrchar\cyrm\cyrchar\cyra\cyrchar\cyrn\cyrchar\cyrii\cyrchar\cyrt\cyrchar\cyrn\cyrchar\cyrii{} \cyrchar\cyrm\cyrchar\cyre\cyrchar\cyrt\cyrchar\cyro\cyrchar\cyrd\cyrchar\cyri{} \cyrchar\cyrt\cyrchar\cyra{} \cyrchar\cyrp\cyrchar\cyrii\cyrchar\cyrd\cyrchar\cyrh\cyrchar\cyro\cyrchar\cyrd\cyrchar\cyri{} \cyrchar\cyrd\cyrchar\cyro{} \cyrchar\cyrv\cyrchar\cyri\cyrchar\cyrya\cyrchar\cyrv\cyrchar\cyrl\cyrchar\cyre\cyrchar\cyrn\cyrchar\cyrn\cyrchar\cyrya{} \cyrchar\cyrp\cyrchar\cyrr\cyrchar\cyro\cyrchar\cyrp\cyrchar\cyra\cyrchar\cyrg\cyrchar\cyra\cyrchar\cyrn\cyrchar\cyrd\cyrchar\cyri\cyrchar\cyrs\cyrchar\cyrsftsn\cyrchar\cyrk\cyrchar\cyri\cyrchar\cyrh{} \cyrchar\cyrn\cyrchar\cyro\cyrchar\cyrv\cyrchar\cyri\cyrchar\cyrn{} \cyrchar\cyrz{} \cyrchar\cyrv\cyrchar\cyri\cyrchar\cyrk\cyrchar\cyro\cyrchar\cyrr\cyrchar\cyri\cyrchar\cyrs\cyrchar\cyrt\cyrchar\cyra\cyrchar\cyrn\cyrchar\cyrn\cyrchar\cyrya\cyrchar\cyrm{} \cyrchar\cyrm\cyrchar\cyre\cyrchar\cyrt\cyrchar\cyro\cyrchar\cyrd\cyrchar\cyrii\cyrchar\cyrv{} \cyrchar\cyro\cyrchar\cyrb\cyrchar\cyrr\cyrchar\cyro\cyrchar\cyrb\cyrchar\cyrk\cyrchar\cyri{} \cyrchar\cyrp\cyrchar\cyrr\cyrchar\cyri\cyrchar\cyrr\cyrchar\cyro\cyrchar\cyrd\cyrchar\cyrn\cyrchar\cyro\cyrchar\cyryi{} \cyrchar\cyrm\cyrchar\cyro\cyrchar\cyrv\cyrchar\cyri. \cyrchar\CYRO\cyrchar\cyrs\cyrchar\cyrn\cyrchar\cyro\cyrchar\cyrv\cyrchar\cyrn\cyrchar\cyri\cyrchar\cyrm{} \cyrchar\cyrz\cyrchar\cyra\cyrchar\cyrv\cyrchar\cyrd\cyrchar\cyra\cyrchar\cyrn\cyrchar\cyrn\cyrchar\cyrya\cyrchar\cyrm{} \cyrchar\cyrie{} \cyrchar\cyrr\cyrchar\cyro\cyrchar\cyrz\cyrchar\cyrr\cyrchar\cyro\cyrchar\cyrb\cyrchar\cyrk\cyrchar\cyra{} \cyrchar\cyrm\cyrchar\cyro\cyrchar\cyrd\cyrchar\cyre\cyrchar\cyrl\cyrchar\cyrii, \cyrchar\cyrk\cyrchar\cyro\cyrchar\cyrt\cyrchar\cyrr\cyrchar\cyra{} \cyrchar\cyrb\cyrchar\cyru\cyrchar\cyrd\cyrchar\cyre{} \cyrchar\cyrz\cyrchar\cyrd\cyrchar\cyra\cyrchar\cyrt\cyrchar\cyrn\cyrchar\cyra{} \cyrchar\cyrv\cyrchar\cyri\cyrchar\cyrz\cyrchar\cyrn\cyrchar\cyra\cyrchar\cyrch\cyrchar\cyra\cyrchar\cyrt\cyrchar\cyri, \cyrchar\cyrch\cyrchar\cyri{} \cyrchar\cyrm\cyrchar\cyrii\cyrchar\cyrs\cyrchar\cyrt\cyrchar\cyri\cyrchar\cyrt\cyrchar\cyrsftsn{} \cyrchar\cyrp\cyrchar\cyro\cyrchar\cyrv\cyrchar\cyrii\cyrchar\cyrd\cyrchar\cyro\cyrchar\cyrm\cyrchar\cyrl\cyrchar\cyre\cyrchar\cyrn\cyrchar\cyrn\cyrchar\cyrya{} \cyrchar\cyro\cyrchar\cyrz\cyrchar\cyrn\cyrchar\cyra\cyrchar\cyrk\cyrchar\cyri{} \cyrchar\cyrm\cyrchar\cyra\cyrchar\cyrn\cyrchar\cyrii\cyrchar\cyrp\cyrchar\cyru\cyrchar\cyrl\cyrchar\cyrya\cyrchar\cyrc\cyrchar\cyrii\cyrchar\cyryi. \cyrchar\CYRD\cyrchar\cyrl\cyrchar\cyrya{} \cyrchar\cyrd\cyrchar\cyro\cyrchar\cyrs\cyrchar\cyrya\cyrchar\cyrg\cyrchar\cyrn\cyrchar\cyre\cyrchar\cyrn\cyrchar\cyrn\cyrchar\cyrya{} \cyrchar\cyrm\cyrchar\cyre\cyrchar\cyrt\cyrchar\cyri{} \cyrchar\cyru{} \cyrchar\cyrr\cyrchar\cyro\cyrchar\cyrb\cyrchar\cyro\cyrchar\cyrt\cyrchar\cyrii{} \cyrchar\cyrb\cyrchar\cyru\cyrchar\cyrd\cyrchar\cyru\cyrchar\cyrt\cyrchar\cyrsftsn{} \cyrchar\cyrp\cyrchar\cyrr\cyrchar\cyro\cyrchar\cyra\cyrchar\cyrn\cyrchar\cyra\cyrchar\cyrl\cyrchar\cyrii\cyrchar\cyrz\cyrchar\cyru\cyrchar\cyrv\cyrchar\cyra\cyrchar\cyrn\cyrchar\cyrii{} \cyrchar\cyrii\cyrchar\cyrs\cyrchar\cyrn\cyrchar\cyru\cyrchar\cyryu\cyrchar\cyrch\cyrchar\cyrii{} \cyrchar\cyrm\cyrchar\cyre\cyrchar\cyrt\cyrchar\cyro\cyrchar\cyrd\cyrchar\cyri{} \cyrchar\cyro\cyrchar\cyrb\cyrchar\cyrr\cyrchar\cyro\cyrchar\cyrb\cyrchar\cyrk\cyrchar\cyri{} \cyrchar\cyrp\cyrchar\cyrr\cyrchar\cyri\cyrchar\cyrr\cyrchar\cyro\cyrchar\cyrd\cyrchar\cyrn\cyrchar\cyro\cyrchar\cyryi{} \cyrchar\cyrm\cyrchar\cyro\cyrchar\cyrv\cyrchar\cyri{} \cyrchar\cyrt\cyrchar\cyra{} \cyrchar\cyra\cyrchar\cyrl\cyrchar\cyrg\cyrchar\cyro\cyrchar\cyrr\cyrchar\cyri\cyrchar\cyrt\cyrchar\cyrm\cyrchar\cyri{} \cyrchar\cyrm\cyrchar\cyra\cyrchar\cyrsh\cyrchar\cyri\cyrchar\cyrn\cyrchar\cyrn\cyrchar\cyro\cyrchar\cyrg\cyrchar\cyro{} \cyrchar\cyrn\cyrchar\cyra\cyrchar\cyrv\cyrchar\cyrch\cyrchar\cyra\cyrchar\cyrn\cyrchar\cyrn\cyrchar\cyrya. \cyrchar\CYRN\cyrchar\cyra{} \cyrchar\cyro\cyrchar\cyrs\cyrchar\cyrn\cyrchar\cyro\cyrchar\cyrv\cyrchar\cyrii{} \cyrchar\cyrc\cyrchar\cyrsftsn\cyrchar\cyro\cyrchar\cyrg\cyrchar\cyro{} \cyrchar\cyrd\cyrchar\cyro\cyrchar\cyrs\cyrchar\cyrl\cyrchar\cyrii\cyrchar\cyrd\cyrchar\cyrzh\cyrchar\cyre\cyrchar\cyrn\cyrchar\cyrn\cyrchar\cyrya{} \cyrchar\cyrb\cyrchar\cyru\cyrchar\cyrd\cyrchar\cyru\cyrchar\cyrt\cyrchar\cyrsftsn{} \cyrchar\cyro\cyrchar\cyrb\cyrchar\cyrr\cyrchar\cyra\cyrchar\cyrn\cyrchar\cyrii{} \cyrchar\cyrn\cyrchar\cyra\cyrchar\cyrishrt\cyrchar\cyre\cyrchar\cyrf\cyrchar\cyre\cyrchar\cyrk\cyrchar\cyrt\cyrchar\cyri\cyrchar\cyrv\cyrchar\cyrn\cyrchar\cyrii\cyrchar\cyrsh\cyrchar\cyrii{} \cyrchar\cyrm\cyrchar\cyre\cyrchar\cyrt\cyrchar\cyro\cyrchar\cyrd\cyrchar\cyri{} \cyrchar\cyrd\cyrchar\cyrl\cyrchar\cyrya{} \cyrchar\cyrv\cyrchar\cyri\cyrchar\cyrr\cyrchar\cyrii\cyrchar\cyrsh\cyrchar\cyre\cyrchar\cyrn\cyrchar\cyrn\cyrchar\cyrya{} \cyrchar\cyrc\cyrchar\cyrii\cyrchar\cyrie\cyrchar\cyryi{} \cyrchar\cyrz\cyrchar\cyra\cyrchar\cyrd\cyrchar\cyra\cyrchar\cyrch\cyrchar\cyrii{} \cyrchar\cyrn\cyrchar\cyra{} \cyrchar\cyro\cyrchar\cyrb\cyrchar\cyrr\cyrchar\cyra\cyrchar\cyrn\cyrchar\cyro\cyrchar\cyrm\cyrchar\cyru{} \cyrchar\cyrp\cyrchar\cyrr\cyrchar\cyri\cyrchar\cyrk\cyrchar\cyrl\cyrchar\cyra\cyrchar\cyrd\cyrchar\cyrii. \cyrchar\CYRR\cyrchar\cyre\cyrchar\cyrz\cyrchar\cyru\cyrchar\cyrl\cyrchar\cyrsftsn\cyrchar\cyrt\cyrchar\cyra\cyrchar\cyrt\cyrchar\cyro\cyrchar\cyrm{} \cyrchar\cyrk\cyrchar\cyrv\cyrchar\cyra\cyrchar\cyrl\cyrchar\cyrii\cyrchar\cyrf\cyrchar\cyrii\cyrchar\cyrk\cyrchar\cyra\cyrchar\cyrc\cyrchar\cyrii\cyrchar\cyrishrt\cyrchar\cyrn\cyrchar\cyro\cyrchar\cyryi{} \cyrchar\cyrr\cyrchar\cyro\cyrchar\cyrb\cyrchar\cyro\cyrchar\cyrt\cyrchar\cyri{} \cyrchar\cyrp\cyrchar\cyro\cyrchar\cyrv\cyrchar\cyri\cyrchar\cyrn\cyrchar\cyrn\cyrchar\cyra{} \cyrchar\cyrb\cyrchar\cyru\cyrchar\cyrt\cyrchar\cyri{} \cyrchar\cyrr\cyrchar\cyro\cyrchar\cyrz\cyrchar\cyrr\cyrchar\cyro\cyrchar\cyrb\cyrchar\cyrl\cyrchar\cyre\cyrchar\cyrn\cyrchar\cyra{} \cyrchar\cyrt\cyrchar\cyra{} \cyrchar\cyrp\cyrchar\cyrr\cyrchar\cyro\cyrchar\cyrt\cyrchar\cyre\cyrchar\cyrs\cyrchar\cyrt\cyrchar\cyro\cyrchar\cyrv\cyrchar\cyra\cyrchar\cyrn\cyrchar\cyra{} \cyrchar\cyrm\cyrchar\cyro\cyrchar\cyrd\cyrchar\cyre\cyrchar\cyrl\cyrchar\cyrsftsn, \cyrchar\cyrya\cyrchar\cyrk\cyrchar\cyra{} \cyrchar\cyrz\cyrchar\cyrd\cyrchar\cyra\cyrchar\cyrt\cyrchar\cyrn\cyrchar\cyra{} \cyrchar\cyre\cyrchar\cyrf\cyrchar\cyre\cyrchar\cyrk\cyrchar\cyrt\cyrchar\cyri\cyrchar\cyrv\cyrchar\cyrn\cyrchar\cyro{} \cyrchar\cyra\cyrchar\cyrn\cyrchar\cyra\cyrchar\cyrl\cyrchar\cyrii\cyrchar\cyrz\cyrchar\cyru\cyrchar\cyrv\cyrchar\cyra\cyrchar\cyrt\cyrchar\cyri{} \cyrchar\cyrt\cyrchar\cyre\cyrchar\cyrk\cyrchar\cyrs\cyrchar\cyrt\cyrchar\cyro\cyrchar\cyrv\cyrchar\cyrii{} \cyrchar\cyrd\cyrchar\cyra\cyrchar\cyrn\cyrchar\cyrii{} \cyrchar\cyrt\cyrchar\cyra{} \cyrchar\cyrv\cyrchar\cyri\cyrchar\cyrya\cyrchar\cyrv\cyrchar\cyrl\cyrchar\cyrya\cyrchar\cyrt\cyrchar\cyri{} \cyrchar\cyrp\cyrchar\cyrr\cyrchar\cyro\cyrchar\cyrp\cyrchar\cyra\cyrchar\cyrg\cyrchar\cyra\cyrchar\cyrn\cyrchar\cyrd\cyrchar\cyri\cyrchar\cyrs\cyrchar\cyrt\cyrchar\cyrs\cyrchar\cyrsftsn\cyrchar\cyrk\cyrchar\cyrii{} \cyrchar\cyrp\cyrchar\cyro\cyrchar\cyrv\cyrchar\cyrii\cyrchar\cyrd\cyrchar\cyro\cyrchar\cyrm\cyrchar\cyrl\cyrchar\cyre\cyrchar\cyrn\cyrchar\cyrn\cyrchar\cyrya.},
  langid = {ukrainian},
  annotation = {Read\_Status: Read\\
Read\_Status\_Date: 2025-10-28T16:08:23.047Z},
  file = {C:\Users\nasty\Documents\Zotero\Яськова - 2023 - Розв‘язання задачі класифікації текстів методами обробки природної мови.pdf}
}

@article{yuanBenchmarkCrossDomainArgumentative2025,
  title = {A {{Benchmark}} for {{Cross-Domain Argumentative Stance Classification}} on {{Social Media}}},
  author = {Yuan, Jiaqing and Xi, Ruijie and Singh, Munindar P.},
  year = 2025,
  month = jun,
  journal = {Proceedings of the International AAAI Conference on Web and Social Media},
  volume = {19},
  pages = {2182--2196},
  issn = {2334-0770},
  doi = {10.1609/icwsm.v19i1.35927},
  urldate = {2025-07-01},
  abstract = {Argumentative stance classification plays a key role in identifying authors' viewpoints on specific topics. However, generating diverse pairs of argumentative sentences across various domains is challenging. Existing benchmarks often come from a single domain or focus on a limited set of topics. Additionally, manual annotation for accurate labeling is time-consuming and labor-intensive. To address these challenges, we propose leveraging platform rules, readily available expert-curated content, and large language models to bypass the need for human annotation. Our approach produces a multidomain benchmark comprising 4,498 topical claims and 30,961 arguments from three sources, spanning 21 domains. We benchmark the dataset in fully supervised, zero-shot, and few-shot settings, shedding light on the strengths and limitations of different methodologies.},
  copyright = {Copyright (c) 2025 Association for the Advancement of Artificial Intelligence},
  langid = {english},
  keywords = {icwsm2025},
  annotation = {Read\_Status: To Read\\
Read\_Status\_Date: 2025-07-03T15:10:53.678Z},
  file = {C:\Users\nasty\Documents\Zotero\Yuan et al. - 2025 - A Benchmark for Cross-Domain Argumentative Stance Classification on Social Media.pdf}
}

@misc{yuanContextGNNTwoTowerRecommendation2024,
  title = {{{ContextGNN}}: {{Beyond Two-Tower Recommendation Systems}}},
  shorttitle = {{{ContextGNN}}},
  author = {Yuan, Yiwen and Zhang, Zecheng and He, Xinwei and Nitta, Akihiro and Hu, Weihua and Wang, Dong and Shah, Manan and Huang, Shenyang and Stojanovi{\v c}, Bla{\v z} and Krumholz, Alan and Lenssen, Jan Eric and Leskovec, Jure and Fey, Matthias},
  year = 2024,
  month = nov,
  number = {arXiv:2411.19513},
  eprint = {2411.19513},
  primaryclass = {cs},
  publisher = {arXiv},
  doi = {10.48550/arXiv.2411.19513},
  urldate = {2024-12-18},
  abstract = {Recommendation systems predominantly utilize two-tower architectures, which evaluate user-item rankings through the inner product of their respective embeddings. However, one key limitation of two-tower models is that they learn a pair-agnostic representation of users and items. In contrast, pair-wise representations either scale poorly due to their quadratic complexity or are too restrictive on the candidate pairs to rank. To address these issues, we introduce Context-based Graph Neural Networks (ContextGNNs), a novel deep learning architecture for link prediction in recommendation systems. The method employs a pair-wise representation technique for familiar items situated within a user's local subgraph, while leveraging two-tower representations to facilitate the recommendation of exploratory items. A final network then predicts how to fuse both pair-wise and two-tower recommendations into a single ranking of items. We demonstrate that ContextGNN is able to adapt to different data characteristics and outperforms existing methods, both traditional and GNN-based, on a diverse set of practical recommendation tasks, improving performance by 20\% on average.},
  archiveprefix = {arXiv},
  keywords = {Computer Science - Information Retrieval,Computer Science - Machine Learning},
  annotation = {Read\_Status: Not Reading\\
Read\_Status\_Date: 2025-07-28T15:29:01.616Z},
  file = {C\:\\Users\\nasty\\Documents\\Zotero\\Yuan et al. - 2024 - ContextGNN Beyond Two-Tower Recommendation Systems 1.pdf;C\:\\Users\\nasty\\Zotero\\storage\\3MF7NVG4\\2411.html}
}

@article{zubiagaDiscourseawareRumourStance2018,
  title = {Discourse-Aware Rumour Stance Classification in Social Media Using Sequential Classifiers},
  author = {Zubiaga, Arkaitz and Kochkina, Elena and Liakata, Maria and Procter, Rob and Lukasik, Michal and Bontcheva, Kalina and Cohn, Trevor and Augenstein, Isabelle},
  year = 2018,
  month = mar,
  journal = {Information Processing \& Management},
  volume = {54},
  number = {2},
  pages = {273--290},
  issn = {0306-4573},
  doi = {10.1016/j.ipm.2017.11.009},
  urldate = {2024-11-21},
  abstract = {Rumour stance classification, defined as classifying the stance of specific social media posts into one of supporting, denying, querying or commenting on an earlier post, is becoming of increasing interest to researchers. While most previous work has focused on using individual tweets as classifier inputs, here we report on the performance of sequential classifiers that exploit the discourse features inherent in social media interactions or `conversational threads'. Testing the effectiveness of four sequential classifiers -- Hawkes Processes, Linear-Chain Conditional Random Fields (Linear CRF), Tree-Structured Conditional Random Fields (Tree CRF) and Long Short Term Memory networks (LSTM) -- on eight datasets associated with breaking news stories, and looking at different types of local and contextual features, our work sheds new light on the development of accurate stance classifiers. We show that sequential classifiers that exploit the use of discourse properties in social media conversations while using only local features, outperform non-sequential classifiers. Furthermore, we show that LSTM using a reduced set of features can outperform the other sequential classifiers; this performance is consistent across datasets and across types of stances. To conclude, our work also analyses the different features under study, identifying those that best help characterise and distinguish between stances, such as supporting tweets being more likely to be accompanied by evidence than denying tweets. We also set forth a number of directions for future research.},
  keywords = {Breaking news,Social media,Stance classification,Veracity classification},
  annotation = {Read\_Status: Not Reading\\
Read\_Status\_Date: 2025-07-28T15:29:18.751Z},
  file = {C:\Users\nasty\Documents\Zotero\Zubiaga et al. - 2018 - Discourse-aware rumour stance classification in social media using sequential classifiers 1.pdf}
}

@inproceedings{park_challenges_2022,
	address = {Abu Dhabi, United Arab Emirates},
	title = {Challenges and {Opportunities} in {Information} {Manipulation} {Detection}: {An} {Examination} of {Wartime} {Russian} {Media}},
	shorttitle = {Challenges and {Opportunities} in {Information} {Manipulation} {Detection}},
	url = {https://aclanthology.org/2022.findings-emnlp.382/},
	doi = {10.18653/v1/2022.findings-emnlp.382},
	abstract = {NLP research on public opinion manipulation campaigns has primarily focused on detecting overt strategies such as fake news and disinformation. However, information manipulation in the ongoing Russia-Ukraine war exemplifies how governments and media also employ more nuanced strategies. We release a new dataset, VoynaSlov, containing 38M+ posts from Russian media outlets on Twitter and VKontakte, as well as public activity and responses, immediately preceding and during the 2022 Russia-Ukraine war. We apply standard and recently-developed NLP models on VoynaSlov to examine agenda setting, framing, and priming, several strategies underlying information manipulation, and reveal variation across media outlet control, social media platform, and time. Our examination of these media effects and extensive discussion of current approaches' limitations encourage further development of NLP models for understanding information manipulation in emerging crises, as well as other real-world and interdisciplinary tasks.},
	urldate = {2025-12-19},
	booktitle = {Findings of the {Association} for {Computational} {Linguistics}: {EMNLP} 2022},
	publisher = {Association for Computational Linguistics},
	author = {Park, Chan Young and Mendelsohn, Julia and Field, Anjalie and Tsvetkov, Yulia},
	editor = {Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue},
	month = dec,
	year = {2022},
	pages = {5209--5235},
	file = {Park et al. - 2022 - Challenges and Opportunities in Information Manipulation Detection An Examination of Wartime Russia 1.pdf:C\:\\Users\\User\\Documents\\Zotero\\Park et al. - 2022 - Challenges and Opportunities in Information Manipulation Detection An Examination of Wartime Russia 1.pdf:application/pdf},
}

@misc{roscoeRussiaClonesWikipedia2024,
  title = {Russia {{Clones Wikipedia}}, {{Censors It}}, {{Bans Original}}},
  author = {Roscoe, Jules},
  year = 2024,
  month = apr,
  journal = {404 Media},
  urldate = {2026-02-10},
  abstract = {Ruviki is intended to be a more ``trustworthy'' source of information for Russians by editing out anything that makes the Russian government look bad.},
  howpublished = {https://www.404media.co/russia-clones-wikipedia-censors-it-bans-original/},
  langid = {english},
  annotation = {Read\_Status: New\\
Read\_Status\_Date: 2026-02-10T14:45:14.157Z},
  file = {C:\Users\nasty\Zotero\storage\87DFUSCY\russia-clones-wikipedia-censors-it-bans-original.html}
}
