% Use this file for citations not found in the ACL Anthology (contained in "anthology.bib").

@book{Aho:72,
    author  = {Alfred V. Aho and Jeffrey D. Ullman},
    title   = {The Theory of Parsing, Translation and Compiling},
    year    = "1972",
    volume  = "1",
    publisher = {Prentice-Hall},
    address = {Englewood Cliffs, NJ}
}

@book{APA:83,
    author  = {{American Psychological Association}},
    title   = {Publications Manual},
    year    = "1983",
    publisher = {American Psychological Association},
    address = {Washington, DC}
}

@article{Chandra:81,
	author = {Ashok K. Chandra and Dexter C. Kozen and Larry J. Stockmeyer},
	year = "1981",
	title = {Alternation},
	journal = {Journal of the Association for Computing Machinery},
	volume = "28",
	number = "1",
	pages = "114--133",
	doi = "10.1145/322234.322243",
}

@inproceedings{andrew2007scalable,
  title={Scalable training of {L1}-regularized log-linear models},
  author={Andrew, Galen and Gao, Jianfeng},
  booktitle={Proceedings of the 24th International Conference on Machine Learning},
  pages={33--40},
  year={2007},
}

@book{Gusfield:97,
    author  = {Dan Gusfield},
    title   = {Algorithms on Strings, Trees and Sequences},
    year    = "1997",
    publisher = {Cambridge University Press},
    address = {Cambridge, UK}
}

@article{rasooli-tetrault-2015,
    author    = {Mohammad Sadegh Rasooli and Joel R. Tetreault},
    title     = {Yara Parser: {A} Fast and Accurate Dependency Parser},
    journal   = {Computing Research Repository},
    volume    = {arXiv:1503.06733},
    year      = {2015},
    url       = {http://arxiv.org/abs/1503.06733},
    note    = {version 2}
}

@article{Ando2005,
	Acmid = {1194905},
	Author = {Ando, Rie Kubota and Zhang, Tong},
	Issn = {1532-4435},
	Issue_Date = {12/1/2005},
	Journal = {Journal of Machine Learning Research},
	Month = dec,
	Numpages = {37},
	Pages = {1817--1853},
	Publisher = {JMLR.org},
	Title = {A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data},
	Volume = {6},
	Year = {2005}
}
@inproceedings{10.1145/3487553.3524665,
    author = {Phillips, Samantha C. and Ng, Lynnette Hui Xian and Carley, Kathleen M.},
    title = {Hoaxes and Hidden agendas: A Twitter Conspiracy Theory Dataset: Data Paper},
    year = {2022},
    isbn = {9781450391306},
    publisher = {Association for Computing Machinery},
    address = {New York, NY, USA},
    url = {https://doi.org/10.1145/3487553.3524665},
    doi = {10.1145/3487553.3524665},
    booktitle = {Companion Proceedings of the Web Conference 2022},
    pages = {876–880},
    numpages = {5},
    keywords = {Twitter, classification, conspiracy theory, dataset, stance detection},
    location = {Virtual Event, Lyon, France},
    series = {WWW '22}
}
@article{doi:10.1177/20563051211043212,
    author = {J. D. Moffitt and Catherine King and Kathleen M. Carley},
    title ={Hunting Conspiracy Theories During the COVID-19 Pandemic},
    
    journal = {Social Media + Society},
    volume = {7},
    number = {3},
    pages = {20563051211043212},
    year = {2021},
    doi = {10.1177/20563051211043212},
    
    URL = { 
            https://doi.org/10.1177/20563051211043212
    },
    eprint = {         
            https://doi.org/10.1177/20563051211043212
    }
}
@misc{faddoul2020longitudinalanalysisyoutubespromotion,
      title={A Longitudinal Analysis of YouTube's Promotion of Conspiracy Videos}, 
      author={Marc Faddoul and Guillaume Chaslot and Hany Farid},
      year={2020},
      eprint={2003.03318},
      archivePrefix={arXiv},
      primaryClass={cs.CY},
      url={https://arxiv.org/abs/2003.03318}
}
@article{Liaw_Huang_Benevenuto_Kwak_An_2023, title={YouNICon: YouTube’s CommuNIty of Conspiracy Videos}, volume={17}, url={https://ojs.aaai.org/index.php/ICWSM/article/view/22218}, DOI={10.1609/icwsm.v17i1.22218}, number={1}, journal={Proceedings of the International AAAI Conference on Web and Social Media}, author={Yi Liaw, Shao and Huang, Fan and Benevenuto, Fabricio and Kwak, Haewoon and An, Jisun}, year={2023}, month={Jun.}, pages={1102-1111} }
@article{GAMBINI202425,
title = {The anatomy of conspiracy theorists: Unveiling traits using a comprehensive Twitter dataset},
journal = {Computer Communications},
volume = {217},
pages = {25-40},
year = {2024},
issn = {0140-3664},
doi = {https://doi.org/10.1016/j.comcom.2024.01.027},
url = {https://www.sciencedirect.com/science/article/pii/S0140366424000264},
author = {Margherita Gambini and Serena Tardelli and Maurizio Tesconi},
keywords = {Conspiracy, Machine learning, Social media dataset, Comparative analysis}
}
@article{10.1111/exsy.13671,
author = {Korenčić, Damir and Chulvi, Berta and Casals, Xavier Bonet and Toselli, Alejandro and Taulé, Mariona and Rosso, Paolo},
title = {What distinguishes conspiracy from critical narratives? A computational analysis of oppositional discourse},
journal = {Expert Systems},
volume = {41},
number = {11},
pages = {e13671},
keywords = {conspiracy theories, NLP for computational social science, oppositional thinking, sequence labelling, text classification},
doi = {https://doi.org/10.1111/exsy.13671},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/exsy.13671},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/exsy.13671},
year = {2024}
}
@article{batzdorfer2022conspiracy,
  title={Conspiracy theories on Twitter: emerging motifs and temporal dynamics during the COVID-19 pandemic},
  author={Batzdorfer, Veronika and Steinmetz, Holger and Biella, Marco and Alizadeh, Meysam},
  journal={International journal of data science and analytics},
  volume={13},
  number={4},
  pages={315--333},
  year={2022},
  publisher={Springer}
}
@inproceedings{batzdorfer2024conspiracy,
  title={Conspiracy Narratives on Voat: A Longitudinal Analysis of Cognitive Activation and Evolutionary Psychology Features},
  author={Batzdorfer, Veronika},
  booktitle={Proceedings of the 16th ACM Web Science Conference},
  pages={42--47},
  year={2024}
}
@article{samory2018government,
  title={'The Government Spies Using Our Webcams' The Language of Conspiracy Theories in Online Discussions},
  author={Samory, Mattia and Mitra, Tanushree},
  journal={Proceedings of the ACM on Human-Computer Interaction},
  volume={2},
  number={CSCW},
  pages={1--24},
  year={2018},
  publisher={ACM New York, NY, USA}
}
@article{10.1177/1745691618774270, 
year = {2018}, 
keywords = {conspiracy}, 
title = {{Conspiracy Theories: Evolved Functions and Psychological Mechanisms}}, 
author = {Prooijen, Jan Willem van and Vugt, Mark van}, 
journal = {Perspectives on Psychological Science}, 
doi = {10.1177/1745691618774270}, 
pmid = {30231213}, 
pages = {770--788}, 
volume = {13}
}
@inproceedings{mitra2016understanding,
  title={Understanding anti-vaccination attitudes in social media},
  author={Mitra, Tanushree and Counts, Scott and Pennebaker, James},
  booktitle={Proceedings of the International AAAI Conference on web and Social Media},
  volume={10},
  number={1},
  pages={269--278},
  year={2016}
}
@article{miani2021loco,
  title={LOCO: The 88-million-word language of conspiracy corpus},
  author={Miani, Alessandro and Hills, Thomas and Bangerter, Adrian},
  journal={Behavior research methods},
  pages={1--24},
  year={2021},
  publisher={Springer}
}
@article{10.3389/fpsyg.2013.00406, 
year = {2013}, 
keywords = {conspiracy}, 
title = {{Thirty shades of truth: Conspiracy theories as stories of individuation, not of pathological delusion}}, 
author = {Raab, Marius Hans and Ortlieb, Stefan Andreas and Auer, Nikolas and Guthmann, Klara and Carbon, Claus-Christian}, 
journal = {Frontiers in Psychology}, 
doi = {10.3389/fpsyg.2013.00406}, 
pmid = {23847576}, 
pages = {1--9}, 
volume = {4}
}
@article{10.1371/journal.pone.0233879, 
year = {2020}, 
title = {{An automated pipeline for the discovery of conspiracy and conspiracy theory narrative frameworks: Bridgegate, Pizzagate and storytelling on the web}}, 
author = {Tangherlini, Timothy R. and Shahsavari, Shadi and Shahbazi, Behnam and Ebrahimzadeh, Ehsan and Roychowdhury, Vwani}, 
journal = {PLoS ONE}, 
doi = {10.1371/journal.pone.0233879}, 
pmid = {32544200}, 
pmcid = {PMC7297331}, 
eprint = {2008.09961}, 
abstract = {{Although a great deal of attention has been paid to how conspiracy theories circulate on social media, and the deleterious effect that they, and their factual counterpart conspiracies, have on political institutions, there has been little computational work done on describing their narrative structures. Predicating our work on narrative theory, we present an automated pipeline for the discovery and description of the generative narrative frameworks of conspiracy theories that circulate on social media, and actual conspiracies reported in the news media. We base this work on two separate comprehensive repositories of blog posts and news articles describing the well-known conspiracy theory Pizzagate from 2016, and the New Jersey political conspiracy Bridgegate from 2013. Inspired by the qualitative narrative theory of Greimas, we formulate a graphical generative machine learning model where nodes represent actors/actants, and multi-edges and self-loops among nodes capture context-specific relationships. Posts and news items are viewed as samples of subgraphs of the hidden narrative framework network. The problem of reconstructing the underlying narrative structure is then posed as a latent model estimation problem. To derive the narrative frameworks in our target corpora, we automatically extract and aggregate the actants (people, places, objects) and their relationships from the posts and articles. We capture context specific actants and interactant relationships by developing a system of supernodes and subnodes. We use these to construct an actant-relationship network, which constitutes the underlying generative narrative framework for each of the corpora. We show how the Pizzagate framework relies on the conspiracy theorists’ interpretation of “hidden knowledge” to link otherwise unlinked domains of human interaction, and hypothesize that this multi-domain focus is an important feature of conspiracy theories. We contrast this to the single domain focus of an actual conspiracy. While Pizzagate relies on the alignment of multiple domains, Bridgegate remains firmly rooted in the single domain of New Jersey politics. We hypothesize that the narrative framework of a conspiracy theory might stabilize quickly in contrast to the narrative framework of an actual conspiracy, which might develop more slowly as revelations come to light. By highlighting the structural differences between the two narrative frameworks, our approach could be used by private and public analysts to help distinguish between conspiracy theories and conspiracies.}}, 
pages = {e0233879}, 
number = {6}, 
volume = {15}, 
keywords = {}
}
@inproceedings{10.1007/s11280-021-00862-x, 
year = {2021}, 
author = {Zhang, Yafei and Wang, Lin and Zhu, Jonathan J. H. and Wang, Xiaofan}, 
title = {{Conspiracy vs science: A large-scale analysis of online discussion cascades}}, 
booktitle = {World Wide Web}, 
doi = {10.1007/s11280-021-00862-x}, 
abstract = {{With the emergence and rapid proliferation of social media platforms and social networking sites, recent years have witnessed a surge of misinformation spreading in our daily life. Drawing on a large-scale dataset which covers more than 1.4M posts and 18M comments from an online social media platform, we investigate the propagation of two distinct narratives–(i) conspiracy information, whose claims are generally unsubstantiated and thus referred as misinformation to some extent, and (ii) scientific information, whose origins are generally readily identifiable and verifiable. We find that conspiracy cascades tend to propagate in a multigenerational branching process whereas science cascades are more likely to grow in a breadth-first manner. Specifically, conspiracy information triggers larger cascades, involves more users and generations, persists longer, and is more viral and bursty than science information. Content analysis reveals that conspiracy cascades contain more negative words and emotional words which convey anger, fear, disgust, surprise and trust. We also find that conspiracy cascades are much more concerned with political and controversial topics. After applying machine learning models, we achieve an AUC score of nearly 90\% in discriminating conspiracy from science narratives using the constructed features. We further investigate user’s role during the growth of cascades. In contrast with previous assumption that misinformation is primarily driven by a small set of users, we find that conspiracy cascades are more likely to be controlled by a broader set of users than science cascades, imposing new challenges on the management of misinformation. Although political affinity is thought to affect the consumption of misinformation, there is very little evidence that political orientation of the information source plays a role during the propagation of conspiracy information; Instead, we find that conspiracy information from media outlets with left or right orientation triggers smaller cascades and is less viral than information from online social media platforms (e.g., Twitter and Imgur) whose political orientations are unclear. Our study provides complementing evidence to current misinformation research and has practical policy implications to stem the propagation and mitigate the influence of misinformation online.}}, 
pages = {585--606}, 
volume = {24}, 
series = {World Wide Web}, 
keywords = {}
}
@book{10.1007/s42001-020-00086-5, 
year = {2020}, 
title = {{Conspiracy in the time of corona: automatic detection of emerging COVID-19 conspiracy theories in social media and the news}}, 
author = {Shahsavari, Shadi and Holur, Pavan and Wang, Tianyi and Tangherlini, Timothy R. and Roychowdhury, Vwani}, 
abstract = {{Rumors and conspiracy theories thrive in environments of low confidence and low trust. Consequently, it is not surprising that ones related to the Covid-19 pandemic are proliferating given the lack of any authoritative scientific consensus on the virus, its spread and containment, or on the long term social and economic ramifications of the pandemic. Among the stories currently circulating are ones suggesting that the 5G network activates the virus, that the pandemic is a hoax perpetrated by a global cabal, that the virus is a bio-weapon released deliberately by the Chinese, or that Bill Gates is using it as cover to launch a global surveillance regime. While some may be quick to dismiss these stories as having little impact on real-world behavior, recent events including the destruction of property, racially fueled attacks against Asian Americans, and demonstrations espousing resistance to public health orders countermand such conclusions. Inspired by narrative theory, we crawl social media sites and news reports and, through the application of automated machine-learning methods, discover the underlying narrative frameworks supporting the generation of these stories. We show how the various narrative frameworks fueling rumors and conspiracy theories rely on the alignment of otherwise disparate domains of knowledge, and consider how they attach to the broader reporting on the pandemic. These alignments and attachments, which can be monitored in near real-time, may be useful for identifying areas in the news that are particularly vulnerable to reinterpretation by conspiracy theorists. Understanding the dynamics of storytelling on social media and the narrative frameworks that provide the generative basis for these stories may also be helpful for devising methods to disrupt their spread.}}, 
volume = {3}, 
keywords = {}, 
doi = {10.1007/s42001-020-00086-5}
}
@article{10.1007/s42001-023-00200-3, 
year = {2023}, 
title = {{COCO: an annotated Twitter dataset of COVID-19 conspiracy theories}}, 
author = {Langguth, Johannes and Schroeder, Daniel Thilo and Filkuková, Petra and Brenner, Stefan and Phillips, Jesper and Pogorelov, Konstantin}, 
journal = {Journal of Computational Social Science}, 
issn = {2432-2717}, 
doi = {10.1007/s42001-023-00200-3}, 
pmid = {37363806}, 
pmcid = {PMC10071453}, 
abstract = {{The COVID-19 pandemic has been accompanied by a surge of misinformation on social media which covered a wide range of different topics and contained many competing narratives, including conspiracy theories. To study such conspiracy theories, we created a dataset of 3495 tweets with manual labeling of the stance of each tweet w.r.t. 12 different conspiracy topics. The dataset thus contains almost 42,000 labels, each of which determined by majority among three expert annotators. The dataset was selected from COVID-19 related Twitter data spanning from January 2020 to June 2021 using a list of 54 keywords. The dataset can be used to train machine learning based classifiers for both stance and topic detection, either individually or simultaneously. BERT was used successfully for the combined task. The dataset can also be used to further study the prevalence of different conspiracy narratives. To this end we qualitatively analyze the tweets, discussing the structure of conspiracy narratives that are frequently found in the dataset. Furthermore, we illustrate the interconnection between the conspiracy categories as well as the keywords.}}, 
pages = {1--42}, 
keywords = {}
}
@article{10.48550/arxiv.2310.18545, 
year = {2023}, 
title = {{Identifying Conspiracy Theories News based on Event Relation Graph}}, 
author = {Lei, Yuanyuan and Huang, Ruihong}, 
journal = {arXiv}, 
doi = {10.48550/arxiv.2310.18545}, 
eprint = {2310.18545}, 
abstract = {{Conspiracy theories, as a type of misinformation, are narratives that explains an event or situation in an irrational or malicious manner. While most previous work examined conspiracy theory in social media short texts, limited attention was put on such misinformation in long news documents. In this paper, we aim to identify whether a news article contains conspiracy theories. We observe that a conspiracy story can be made up by mixing uncorrelated events together, or by presenting an unusual distribution of relations between events. Achieving a contextualized understanding of events in a story is essential for detecting conspiracy theories. Thus, we propose to incorporate an event relation graph for each article, in which events are nodes, and four common types of event relations, coreference, temporal, causal, and subevent relations, are considered as edges. Then, we integrate the event relation graph into conspiracy theory identification in two ways: an event-aware language model is developed to augment the basic language model with the knowledge of events and event relations via soft labels; further, a heterogeneous graph attention network is designed to derive a graph embedding based on hard labels. Experiments on a large benchmark dataset show that our approach based on event relation graph improves both precision and recall of conspiracy theory identification, and generalizes well for new unseen media sources.}}, 
keywords = {}
}

@inproceedings{chen-etal-2022-semeval,
    title = "{S}em{E}val-2022 Task 8: Multilingual news article similarity",
    author = {Chen, Xi  and
      Zeynali, Ali  and
      Camargo, Chico  and
      Fl{\"o}ck, Fabian  and
      Gaffney, Devin  and
      Grabowicz, Przemyslaw  and
      Hale, Scott A.  and
      Jurgens, David  and
      Samory, Mattia},
    editor = "Emerson, Guy  and
      Schluter, Natalie  and
      Stanovsky, Gabriel  and
      Kumar, Ritesh  and
      Palmer, Alexis  and
      Schneider, Nathan  and
      Singh, Siddharth  and
      Ratan, Shyam",
    booktitle = "Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)",
    month = jul,
    year = "2022",
    address = "Seattle, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.semeval-1.155/",
    doi = "10.18653/v1/2022.semeval-1.155",
    pages = "1094--1106",
    abstract = "Thousands of new news articles appear daily in outlets in different languages. Understanding which articles refer to the same story can not only improve applications like news aggregation but enable cross-linguistic analysis of media consumption and attention. However, assessing the similarity of stories in news articles is challenging due to the different dimensions in which a story might vary, e.g., two articles may have substantial textual overlap but describe similar events that happened years apart. To address this challenge, we introduce a new dataset of nearly 10,000 news article pairs spanning 18 language combinations annotated for seven dimensions of similarity as SemEval 2022 Task 8. Here, we present an overview of the task, the best performing submissions, and the frontiers and challenges for measuring multilingual news article similarity. While the participants of this SemEval task contributed very strong models, achieving up to 0.818 correlation with gold standard labels across languages, human annotators are capable of reaching higher correlations, suggesting space for further progress."
}

@inproceedings{abufarhaSemEval2022Task62022,
  title = {{{SemEval-2022 Task}} 6: {{iSarcasmEval}}, {{Intended Sarcasm Detection}} in {{English}} and {{Arabic}}},
  shorttitle = {{{SemEval-2022 Task}} 6},
  booktitle = {Proceedings of the 16th {{International Workshop}} on {{Semantic Evaluation}} ({{SemEval-2022}})},
  author = {Abu Farha, Ibrahim and Oprea, Silviu Vlad and Wilson, Steven and Magdy, Walid},
  editor = {Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam},
  year = {2022},
  month = jul,
  pages = {802--814},
  publisher = {Association for Computational Linguistics},
  address = {Seattle, United States},
  doi = {10.18653/v1/2022.semeval-1.111},
  urldate = {2025-03-26},
  abstract = {iSarcasmEval is the first shared task to target intended sarcasm detection: the data for this task was provided and labelled by the authors of the texts themselves. Such an approach minimises the downfalls of other methods to collect sarcasm data, which rely on distant supervision or third-party annotations. The shared task contains two languages, English and Arabic, and three subtasks: sarcasm detection, sarcasm category classification, and pairwise sarcasm identification given a sarcastic sentence and its non-sarcastic rephrase. The task received submissions from 60 different teams, with the sarcasm detection task being the most popular. Most of the participating teams utilised pre-trained language models. In this paper, we provide an overview of the task, data, and participating teams.},
  file = {C:\Users\felix\OneDrive - University College London\Data-Share_UCL\ZoteroLibrary\storage\28YCU8ZF\Abu Farha et al. - 2022 - SemEval-2022 Task 6 iSarcasmEval, Intended Sarcas.pdf}
}

@misc{dimitrovSemEval2021Task62021,
  title = {{{SemEval-2021 Task}} 6: {{Detection}} of {{Persuasion Techniques}} in {{Texts}} and {{Images}}},
  shorttitle = {{{SemEval-2021 Task}} 6},
  author = {Dimitrov, Dimitar and Ali, Bishr Bin and Shaar, Shaden and Alam, Firoj and Silvestri, Fabrizio and Firooz, Hamed and Nakov, Preslav and Martino, Giovanni Da San},
  year = {2021},
  month = apr,
  number = {arXiv:2105.09284},
  eprint = {2105.09284},
  primaryclass = {cs},
  publisher = {arXiv},
  doi = {10.48550/arXiv.2105.09284},
  urldate = {2025-03-26},
  abstract = {We describe SemEval-2021 task 6 on Detection of Persuasion Techniques in Texts and Images: the data, the annotation guidelines, the evaluation setup, the results, and the participating systems. The task focused on memes and had three subtasks: (i) detecting the techniques in the text, (ii) detecting the text spans where the techniques are used, and (iii) detecting techniques in the entire meme, i.e., both in the text and in the image. It was a popular task, attracting 71 registrations, and 22 teams that eventually made an official submission on the test set. The evaluation results for the third subtask confirmed the importance of both modalities, the text and the image. Moreover, some teams reported benefits when not just combining the two modalities, e.g., by using early or late fusion, but rather modeling the interaction between them in a joint model.},
  archiveprefix = {arXiv},
  langid = {english},
  keywords = {Computer Science - Computation and Language,Computer Science - Machine Learning,Computer Science - Multimedia},
  file = {C:\Users\felix\OneDrive - University College London\Data-Share_UCL\ZoteroLibrary\storage\IZKX28TR\Dimitrov et al. - 2021 - SemEval-2021 Task 6 Detection of Persuasion Techn.pdf}
}

@inproceedings{fersiniSemEval2022Task52022,
  title = {{{SemEval-2022 Task}} 5: {{Multimedia Automatic Misogyny Identification}}},
  shorttitle = {{{SemEval-2022 Task}} 5},
  booktitle = {Proceedings of the 16th {{International Workshop}} on {{Semantic Evaluation}} ({{SemEval-2022}})},
  author = {Fersini, Elisabetta and Gasparini, Francesca and Rizzi, Giulia and Saibene, Aurora and Chulvi, Berta and Rosso, Paolo and Lees, Alyssa and Sorensen, Jeffrey},
  editor = {Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam},
  year = {2022},
  month = jul,
  pages = {533--549},
  publisher = {Association for Computational Linguistics},
  address = {Seattle, United States},
  doi = {10.18653/v1/2022.semeval-1.74},
  urldate = {2025-03-26},
  abstract = {The paper describes the SemEval-2022 Task 5: Multimedia Automatic Misogyny Identification (MAMI),which explores the detection of misogynous memes on the web by taking advantage of available texts and images. The task has been organised in two related sub-tasks: the first one is focused on recognising whether a meme is misogynous or not (Sub-task A), while the second one is devoted to recognising types of misogyny (Sub-task B). MAMI has been one of the most popular tasks at SemEval-2022 with more than 400 participants, 65 teams involved in Sub-task A and 41 in Sub-task B from 13 countries. The MAMI challenge received 4214 submitted runs (of which 166 uploaded on the leader-board), denoting an enthusiastic participation for the proposed problem. The collection and annotation is described for the task dataset. The paper provides an overview of the systems proposed for the challenge, reports the results achieved in both sub-tasks and outlines a description of the main errors for a comprehension of the systems capabilities and for detailing future research perspectives.},
  file = {C:\Users\felix\OneDrive - University College London\Data-Share_UCL\ZoteroLibrary\storage\4WPFSKUX\Fersini et al. - 2022 - SemEval-2022 Task 5 Multimedia Automatic Misogyny.pdf}
}

@misc{kirkSemEval2023Task102023,
  title = {{{SemEval-2023 Task}} 10: {{Explainable Detection}} of {{Online Sexism}}},
  shorttitle = {{{SemEval-2023 Task}} 10},
  author = {Kirk, Hannah Rose and Yin, Wenjie and Vidgen, Bertie and R{\"o}ttger, Paul},
  year = {2023},
  month = may,
  number = {arXiv:2303.04222},
  eprint = {2303.04222},
  primaryclass = {cs},
  publisher = {arXiv},
  doi = {10.48550/arXiv.2303.04222},
  urldate = {2025-03-26},
  abstract = {Online sexism is a widespread and harmful phenomenon. Automated tools can assist the detection of sexism at scale. Binary detection, however, disregards the diversity of sexist content, and fails to provide clear explanations for why something is sexist. To address this issue, we introduce SemEval Task 10 on the Explainable Detection of Online Sexism (EDOS). We make three main contributions: i) a novel hierarchical taxonomy of sexist content, which includes granular vectors of sexism to aid explainability; ii) a new dataset of 20,000 social media comments with fine-grained labels, along with larger unlabelled datasets for model adaptation; and iii) baseline models as well as an analysis of the methods, results and errors for participant submissions to our task.},
  archiveprefix = {arXiv},
  langid = {english},
  keywords = {Computer Science - Computation and Language,Computer Science - Computers and Society},
  file = {C:\Users\felix\OneDrive - University College London\Data-Share_UCL\ZoteroLibrary\storage\ZDSLJNH5\Kirk et al. - 2023 - SemEval-2023 Task 10 Explainable Detection of Onl.pdf}
}

@misc{madabushiSemEval2022Task22022,
  title = {{{SemEval-2022 Task}} 2: {{Multilingual Idiomaticity Detection}} and {{Sentence Embedding}}},
  shorttitle = {{{SemEval-2022 Task}} 2},
  author = {Madabushi, Harish Tayyar and {Gow-Smith}, Edward and Garcia, Marcos and Scarton, Carolina and Idiart, Marco and Villavicencio, Aline},
  year = {2022},
  month = may,
  number = {arXiv:2204.10050},
  eprint = {2204.10050},
  primaryclass = {cs},
  publisher = {arXiv},
  doi = {10.48550/arXiv.2204.10050},
  urldate = {2025-03-26},
  abstract = {This paper presents the shared task on Multilingual Idiomaticity Detection and Sentence Embedding, which consists of two Subtasks: (a) a binary classification task aimed at identifying whether a sentence contains an idiomatic expression, and (b) a task based on semantic text similarity which requires the model to adequately represent potentially idiomatic expressions in context. Each Subtask includes different settings regarding the amount of training data. Besides the task description, this paper introduces the datasets in English, Portuguese, and Galician and their annotation procedure, the evaluation metrics, and a summary of the participant systems and their results. The task had close to 100 registered participants organised into twenty five teams making over 650 and 150 submissions in the practice and evaluation phases respectively.},
  archiveprefix = {arXiv},
  langid = {english},
  keywords = {Computer Science - Computation and Language},
  file = {C:\Users\felix\OneDrive - University College London\Data-Share_UCL\ZoteroLibrary\storage\DXLU27PU\Madabushi et al. - 2022 - SemEval-2022 Task 2 Multilingual Idiomaticity Det.pdf}
}

@inproceedings{meaneySemEval2021Task2021,
  title = {{{SemEval}} 2021 {{Task}} 7: {{HaHackathon}}, {{Detecting}} and {{Rating Humor}} and {{Offense}}},
  shorttitle = {{{SemEval}} 2021 {{Task}} 7},
  booktitle = {Proceedings of the 15th {{International Workshop}} on {{Semantic Evaluation}} ({{SemEval-2021}})},
  author = {Meaney, J. A. and Wilson, Steven and Chiruzzo, Luis and Lopez, Adam and Magdy, Walid},
  editor = {Palmer, Alexis and Schneider, Nathan and Schluter, Natalie and Emerson, Guy and Herbelot, Aurelie and Zhu, Xiaodan},
  year = {2021},
  month = aug,
  pages = {105--119},
  publisher = {Association for Computational Linguistics},
  address = {Online},
  doi = {10.18653/v1/2021.semeval-1.9},
  urldate = {2025-03-26},
  abstract = {SemEval 2021 Task 7, HaHackathon, was the first shared task to combine the previously separate domains of humor detection and offense detection. We collected 10,000 texts from Twitter and the Kaggle Short Jokes dataset, and had each annotated for humor and offense by 20 annotators aged 18-70. Our subtasks were binary humor detection, prediction of humor and offense ratings, and a novel controversy task: to predict if the variance in the humor ratings was higher than a specific threshold. The subtasks attracted 36-58 submissions, with most of the participants choosing to use pre-trained language models. Many of the highest performing teams also implemented additional optimization techniques, including task-adaptive training and adversarial training. The results suggest that the participating systems are well suited to humor detection, but that humor controversy is a more challenging task. We discuss which models excel in this task, which auxiliary techniques boost their performance, and analyze the errors which were not captured by the best systems.},
  file = {C:\Users\felix\OneDrive - University College London\Data-Share_UCL\ZoteroLibrary\storage\UFEI6C8E\Meaney et al. - 2021 - SemEval 2021 Task 7 HaHackathon, Detecting and Ra.pdf}
}

@inproceedings{pavlopoulosSemEval2021Task52021,
  title = {{{SemEval-2021 Task}} 5: {{Toxic Spans Detection}}},
  shorttitle = {{{SemEval-2021 Task}} 5},
  booktitle = {Proceedings of the 15th {{International Workshop}} on {{Semantic Evaluation}} ({{SemEval-2021}})},
  author = {Pavlopoulos, John and Sorensen, Jeffrey and Laugier, L{\'e}o and Androutsopoulos, Ion},
  editor = {Palmer, Alexis and Schneider, Nathan and Schluter, Natalie and Emerson, Guy and Herbelot, Aurelie and Zhu, Xiaodan},
  year = {2021},
  month = aug,
  pages = {59--69},
  publisher = {Association for Computational Linguistics},
  address = {Online},
  doi = {10.18653/v1/2021.semeval-1.6},
  urldate = {2025-03-26},
  abstract = {The Toxic Spans Detection task of SemEval-2021 required participants to predict the spans of toxic posts that were responsible for the toxic label of the posts. The task could be addressed as supervised sequence labeling, using training data with gold toxic spans provided by the organisers. It could also be treated as rationale extraction, using classifiers trained on potentially larger external datasets of posts manually annotated as toxic or not, without toxic span annotations. For the supervised sequence labeling approach and evaluation purposes, posts previously labeled as toxic were crowd-annotated for toxic spans. Participants submitted their predicted spans for a held-out test set and were scored using character-based F1. This overview summarises the work of the 36 teams that provided system descriptions.},
  file = {C:\Users\felix\OneDrive - University College London\Data-Share_UCL\ZoteroLibrary\storage\ZFT6HDA7\Pavlopoulos et al. - 2021 - SemEval-2021 Task 5 Toxic Spans Detection.pdf}
}

@inproceedings{perez-almendrosSemEval2022Task42022,
  title = {{{SemEval-2022 Task}} 4: {{Patronizing}} and {{Condescending Language Detection}}},
  shorttitle = {{{SemEval-2022 Task}} 4},
  booktitle = {Proceedings of the 16th {{International Workshop}} on {{Semantic Evaluation}} ({{SemEval-2022}})},
  author = {{Perez-Almendros}, Carla and {Espinosa-Anke}, Luis and Schockaert, Steven},
  year = {2022},
  pages = {298--307},
  publisher = {Association for Computational Linguistics},
  address = {Seattle, United States},
  doi = {10.18653/v1/2022.semeval-1.38},
  urldate = {2025-03-26},
  abstract = {This paper presents an overview of Task 4 at SemEval-2022, which was focused on detecting Patronizing and Condescending Language (PCL) towards vulnerable communities. Two sub-tasks were considered: a binary classification task, where participants needed to classify a given paragraph as containing PCL or not, and a multi-label classification task, where participants needed to identify which types of PCL are present (if any). The task attracted 77 teams. We provide an overview of how the task was organized, discuss the techniques that were employed by the different participants, and summarize the main resulting insights about PCL detection and categorization.},
  langid = {english},
  file = {C:\Users\felix\OneDrive - University College London\Data-Share_UCL\ZoteroLibrary\storage\EF6WITXP\Perez-Almendros et al. - 2022 - SemEval-2022 Task 4 Patronizing and Condescending.pdf}
}

@inproceedings{piskorskiSemEval2023Task32023,
  title = {{{SemEval-2023 Task}} 3: {{Detecting}} the {{Category}}, the {{Framing}}, and the {{Persuasion Techniques}} in {{Online News}} in a {{Multi-lingual Setup}}},
  shorttitle = {{{SemEval-2023 Task}} 3},
  booktitle = {Proceedings of the 17th {{International Workshop}} on {{Semantic Evaluation}} ({{SemEval-2023}})},
  author = {Piskorski, Jakub and Stefanovitch, Nicolas and Da San Martino, Giovanni and Nakov, Preslav},
  editor = {Ojha, Atul Kr. and Do{\u g}ru{\"o}z, A. Seza and Da San Martino, Giovanni and Tayyar Madabushi, Harish and Kumar, Ritesh and Sartori, Elisa},
  year = {2023},
  month = jul,
  pages = {2343--2361},
  publisher = {Association for Computational Linguistics},
  address = {Toronto, Canada},
  doi = {10.18653/v1/2023.semeval-1.317},
  urldate = {2025-03-26},
  abstract = {We describe SemEval-2023 task 3 on Detecting the Category, the Framing, and the Persuasion Techniques in Online News in a Multilingual Setup: the dataset, the task organization process, the evaluation setup, the results, and the participating systems. The task focused on news articles in nine languages (six known to the participants upfront: English, French, German, Italian, Polish, and Russian), and three additional ones revealed to the participants at the testing phase: Spanish, Greek, and Georgian). The task featured three subtasks: (1) determining the genre of the article (opinion, reporting, or satire), (2) identifying one or more frames used in an article from a pool of 14 generic frames, and (3) identify the persuasion techniques used in each paragraph of the article, using a taxonomy of 23 persuasion techniques. This was a very popular task: a total of 181 teams registered to participate, and 41 eventually made an official submission on the test set.},
  file = {C:\Users\felix\OneDrive - University College London\Data-Share_UCL\ZoteroLibrary\storage\NMYNWCPB\Piskorski et al. - 2023 - SemEval-2023 Task 3 Detecting the Category, the F.pdf}
}

@inproceedings{mihaylovaSemEval2019Task82019,
  title = {{{SemEval-2019 Task}} 8: {{Fact Checking}} in {{Community Question Answering Forums}}},
  shorttitle = {{{SemEval-2019 Task}} 8},
  booktitle = {Proceedings of the 13th {{International Workshop}} on {{Semantic Evaluation}}},
  author = {Mihaylova, Tsvetomila and Karadzhov, Georgi and Atanasova, Pepa and Baly, Ramy and Mohtarami, Mitra and Nakov, Preslav},
  editor = {May, Jonathan and Shutova, Ekaterina and Herbelot, Aurelie and Zhu, Xiaodan and Apidianaki, Marianna and Mohammad, Saif M.},
  year = {2019},
  month = jun,
  pages = {860--869},
  publisher = {Association for Computational Linguistics},
  address = {Minneapolis, Minnesota, USA},
  doi = {10.18653/v1/S19-2149},
  urldate = {2025-03-26},
  abstract = {We present SemEval-2019 Task 8 on Fact Checking in Community Question Answering Forums, which features two subtasks. Subtask A is about deciding whether a question asks for factual information vs. an opinion/advice vs. just socializing. Subtask B asks to predict whether an answer to a factual question is true, false or not a proper answer. We received 17 official submissions for subtask A and 11 official submissions for Subtask B. For subtask A, all systems improved over the majority class baseline. For Subtask B, all systems were below a majority class baseline, but several systems were very close to it. The leaderboard and the data from the competition can be found at http://competitions.codalab.org/competitions/20022.},
  file = {C:\Users\felix\OneDrive - University College London\Data-Share_UCL\ZoteroLibrary\storage\VSPN235M\Mihaylova et al. - 2019 - SemEval-2019 Task 8 Fact Checking in Community Qu.pdf}
}


@inproceedings{samory-etal-2026-semeval,
    title = "{S}em{E}val-2026 Task 10: {P}sy{C}o{M}ark -- Psycholinguistic Conspiracy Marker Extraction and Detection",
    author = "Samory, Mattia  and
        Soldner, Felix  and
        Batzdorfer, Veronika",
    editor = "Zampieri, Marcos and
        Ghosh, Debanjan and
        North, Kai and
        Malmasi, Shervin and
        Kochmar, Ekaterina",
    booktitle = "Proceedings of the 20th International Workshop on Semantic Evaluation (SemEval-2026)",
    year = "2026",
    month = jul,
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics"
}

@inproceedings{vazquez-etal-2025-semeval,
    title = "{S}em{E}val-2025 Task 3: Mu-{SHROOM}, the Multilingual Shared-task on Hallucinations and Related Observable Overgeneration Mistakes",
    author = {Vazquez, Raul  and
      Mickus, Timothee  and
      Zosa, Elaine  and
      Vahtola, Teemu  and
      Tiedemann, J{\"o}rg  and
      Sinha, Aman  and
      Segonne, Vincent  and
      Sanchez - Vega, Fernando  and
      Raganato, Alessandro  and
      Libovick{\'y}, Jind{\v{r}}ich  and
      Karlgren, Jussi  and
      Ji, Shaoxiong  and
      Helcl, Jind{\v{r}}ich  and
      Guillou, Liane  and
      De Gibert, Ona  and
      Bengoetxea, Jaione  and
      Attieh, Joseph  and
      Apidianaki, Marianna},
    editor = "Rosenthal, Sara  and
      Ros{\'a}, Aiala  and
      Ghosh, Debanjan  and
      Zampieri, Marcos",
    booktitle = "Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.semeval-1.322/",
    pages = "2472--2497",
    ISBN = "979-8-89176-273-2",
    abstract = "We present the Mu-SHROOM shared task which is focused on detecting hallucinations and other overgeneration mistakes in the output of instruction-tuned large language models (LLMs).Mu-SHROOM addresses general-purpose LLMs in 14 languages, and frames the hallucination detection problem as a span-labeling task. We received 2,618 submissions from 43 participating teams employing diverse methodologies. The very high number of submissions highlights the interest of the community in hallucination detection. We present the results of the participating systems and provide an empirical analysis in order to better understand the factors that can lead to strong performance in this task. We also underscore current challenges, notably the varying degree of hallucinations across languages and the high annotator disagreement when labeling hallucination spans."
}

@inproceedings{dror-etal-2018-hitchhikers,
    title = "The Hitchhiker{'}s Guide to Testing Statistical Significance in Natural Language Processing",
    author = "Dror, Rotem  and
      Baumer, Gili  and
      Shlomov, Segev  and
      Reichart, Roi",
    editor = "Gurevych, Iryna  and
      Miyao, Yusuke",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/P18-1128/",
    doi = "10.18653/v1/P18-1128",
    pages = "1383--1392",
    abstract = "Statistical significance testing is a standard statistical tool designed to ensure that experimental results are not coincidental. In this opinion/ theoretical paper we discuss the role of statistical significance testing in Natural Language Processing (NLP) research. We establish the fundamental concepts of significance testing and discuss the specific aspects of NLP tasks, experimental setups and evaluation measures that affect the choice of significance tests in NLP research. Based on this discussion we propose a simple practical protocol for statistical significance test selection in NLP setups and accompany this protocol with a brief survey of the most relevant tests. We then survey recent empirical papers published in ACL and TACL during 2017 and show that while our community assigns great value to experimental results, statistical significance testing is often ignored or misused. We conclude with a brief discussion of open issues that should be properly addressed so that this important tool can be applied. in NLP research in a statistically sound manner."
}

@inproceedings{zheng-yang-2026-njustkmg,
    title = "{NJUST}{\_}{KMG} at {S}em{E}val-2026 Task 10: {P}sy{C}o{M}ark -- Subtask 2: Conspiracy Detection",
    author = "Zheng, Yuhan and
        Yang, Yang",
    booktitle = "Proceedings of the 20th International Workshop on Semantic Evaluation (SemEval-2026)",
    year = "2026",
    month = jul,
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics"
}

@inproceedings{macko-2026-mdok,
    title = "{mdok-style} at {S}em{E}val-2026 Task 10: Finetuning {LLM}s for Conspiracy Detection",
    author = "Macko, Dominik",
    booktitle = "Proceedings of the 20th International Workshop on Semantic Evaluation (SemEval-2026)",
    year = "2026",
    month = jul,
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics"
}

@inproceedings{dang-2026-dangphuduy,
    title = "{dangphuduy} at {S}em{E}val-2026 Task 10: Span-based Conspiracy Marker Extraction and Emotion-Aware Detection via Gated Fusion",
    author = "Dang Phu, Duy",
    booktitle = "Proceedings of the 20th International Workshop on Semantic Evaluation (SemEval-2026)",
    year = "2026",
    month = jul,
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics"
}

@inproceedings{solanki-etal-2026-varh,
    title = "{VARH-AI} at {S}em{E}val-2026 Task 10: Exploiting Architectural Diversity with Transformer-SSM Ensembles and Confidence-Based Iterative Refinement for Conspiracy Detection",
    author = "Solanki, Hritav Singh and
        Sharma, Shubham and
        Prasad, Manish",
    booktitle = "Proceedings of the 20th International Workshop on Semantic Evaluation (SemEval-2026)",
    year = "2026",
    month = jul,
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics"
}

@inproceedings{gomez-navalon-2026-umuteam,
    title = "{UMUTeam} at {S}em{E}val-2026 Task 10: Transformer Ensembles for Conspiratorial Span Extraction and Detection",
    author = "Gomez Navalon, Jorge",
    booktitle = "Proceedings of the 20th International Workshop on Semantic Evaluation (SemEval-2026)",
    year = "2026",
    month = jul,
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics"
}

@inproceedings{kashaf-etal-2026-hu,
    title = "{HU} at {S}em{E}val-2026 Task 10: Leveraging {GLiNER} with Descriptive Prompts for Conspiracy Element Extraction",
    author = "Kashaf, Muhammad Quddussi and
        Zeeshan, Marium and
        Chaudhry, Shahmir Mustafa",
    booktitle = "Proceedings of the 20th International Workshop on Semantic Evaluation (SemEval-2026)",
    year = "2026",
    month = jul,
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics"
}

@inproceedings{yamaga-etal-2026-curiosai,
    title = "{CuriosAI} at {S}em{E}val-2026 Task 10: Hybrid Approaches to Conspiracy Span Extraction and Binary Detection",
    author = "Yamaga, Daichi and
        Beppu, Fumika and
        Shibata, Yuki and
        Manoj Kumar, Aiswariya and
        Hori, Takayuki",
    booktitle = "Proceedings of the 20th International Workshop on Semantic Evaluation (SemEval-2026)",
    year = "2026",
    month = jul,
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics"
}

@inproceedings{akram-fatima-2026-nust,
    title = "{NUST PsyAI} at {S}em{E}val-2026 Task 10: A Comparative Evaluation of Psycholinguistic Features, Machine Learning, and {LLM}s for Conspiracy Marker Extraction and Detection",
    author = "Akram, Mian M. Husnain and
        Fatima, Mehwish",
    booktitle = "Proceedings of the 20th International Workshop on Semantic Evaluation (SemEval-2026)",
    year = "2026",
    month = jul,
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics"
}

@inproceedings{chakraborty-etal-2026-csecu,
    title = "{CSECU-DSG} at {S}em{E}val-2026 Task 10: Fine-Tuning {DeBERTa} Transformer Model for Conspiracy Detection",
    author = "Chakraborty, Debashish and
        Tabassum, Sumaiya and
        Ibnath, Sabrina and
        Chy, Abu Nowshed",
    booktitle = "Proceedings of the 20th International Workshop on Semantic Evaluation (SemEval-2026)",
    year = "2026",
    month = jul,
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics"
}

@inproceedings{vazquez-cerrillo-etal-2026-late,
    title = "{LATE-iimas} at {S}em{E}val-2026 Task 10: Conspiracy Detection with Ensemble Models and Loss Optimization",
    author = "V{\'a}zquez-Cerrillo, Jos{\'e}-Jorge and
        G{\'o}mez-Adorno, Helena and
        Bel Enguix, Gemma",
    booktitle = "Proceedings of the 20th International Workshop on Semantic Evaluation (SemEval-2026)",
    year = "2026",
    month = jul,
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics"
}

@inproceedings{rodriguez-guierrez-2026-davidinfotec,
    title = "{davidinfotec} at {S}em{E}val-2026 Task 10: From Lexical Baselines to Resource-Efficient Contextual Transformers for Conspiracy Detection",
    author = "Rodriguez Guierrez, David",
    booktitle = "Proceedings of the 20th International Workshop on Semantic Evaluation (SemEval-2026)",
    year = "2026",
    month = jul,
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics"
}

@inproceedings{carabas-etal-2026-psydetectives,
    title = "{psy detectives} at {S}em{E}val-2026 Task 10: {P}sy{C}o{M}ark -- Psycholinguistic Conspiracy Marker Extraction and Detection",
    author = "Caraba{s}, Roxana and
        Nacu, Anamaria Persida and
        Lucian-Constantin, Isac and
        G{\^\i}fu, Daniela",
    booktitle = "Proceedings of the 20th International Workshop on Semantic Evaluation (SemEval-2026)",
    year = "2026",
    month = jul,
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics"
}

@inproceedings{spanakis-etal-2026-ails,
    title = "{AILS-NTUA} at {S}em{E}val-2026 Task 10: Agentic {LLM}s for Psycholinguistic Marker Extraction and Conspiracy Endorsement Detection",
    author = "Spanakis, Panagiotis and
        Lymperaiou, Maria and
        Filandrianos, Giorgos and
        Voulodimos, Athanasios and
        Stamou, Giorgos",
    booktitle = "Proceedings of the 20th International Workshop on Semantic Evaluation (SemEval-2026)",
    year = "2026",
    month = jul,
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics"
}

@inproceedings{goyal-2026-truthgradient,
    title = "{TruthGradient} at {S}em{E}val-2026 Task 10: Dual-Pooling {DeBERTa} for Conspiracy Theory Detection",
    author = "Goyal, Ekansh",
    booktitle = "Proceedings of the 20th International Workshop on Semantic Evaluation (SemEval-2026)",
    year = "2026",
    month = jul,
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics"
}

@inproceedings{wang-tan-2026-wangkongqiang,
    title = "{wangkongqiang} at {S}em{E}val-2026 Task 10: {P}sy{C}o{M}ark -- Psycholinguistic Conspiracy Marker Extraction and Detection",
    author = "Wang, Kongqiang and
        Tan, Qingli",
    booktitle = "Proceedings of the 20th International Workshop on Semantic Evaluation (SemEval-2026)",
    year = "2026",
    month = jul,
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics"
}

@inproceedings{marchitan-2026-unibuc,
    title = "{Unibuc-NLP} at {S}em{E}val-2026 Task 10: Unmasking Conspiracies with Pre-Trained Language Models",
    author = "Marchitan, Teodor-George",
    booktitle = "Proceedings of the 20th International Workshop on Semantic Evaluation (SemEval-2026)",
    year = "2026",
    month = jul,
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics"
}

@inproceedings{takahashi-2026-hidetsune,
    title = "{Hidetsune} at {S}em{E}val-2026 Task 10: A Systematic Exploration of Training and Inference Strategies for Detecting Conspiracy Beliefs",
    author = "Takahashi, Hidetsune",
    booktitle = "Proceedings of the 20th International Workshop on Semantic Evaluation (SemEval-2026)",
    year = "2026",
    month = jul,
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics"
}

@inproceedings{pan-2026-teama,
    title = "Team A at {S}em{E}val-2026 Task 10: Conspiracy Extraction and Classification",
    author = "Pan, Xintong",
    booktitle = "Proceedings of the 20th International Workshop on Semantic Evaluation (SemEval-2026)",
    year = "2026",
    month = jul,
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics"
}

@inproceedings{wang-chen-2026-ccnu,
    title = "{CCNU} at {S}em{E}val-2026 Task 10: Conspiracy Marker Extraction and Detection via Multi-task Learning and {LLM}-based Data Augmentation",
    author = "Wang, Zijun and
        Chen, Guanyi",
    booktitle = "Proceedings of the 20th International Workshop on Semantic Evaluation (SemEval-2026)",
    year = "2026",
    month = jul,
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics"
}

@inproceedings{marhoefer-etal-2026-ucsc,
    title = "{UCSC NLP} at {S}em{E}val-2026 Task 10: Boundary-Aware Span Extraction and Stratified Classification for Conspiracy Detection",
    author = "Marhoefer, Dom and
        Grant-Richards, Glenn and
        Pinero, Aidan and
        Suvakovic, Milos and
        King, Ryan",
    booktitle = "Proceedings of the 20th International Workshop on Semantic Evaluation (SemEval-2026)",
    year = "2026",
    month = jul,
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics"
}

@inproceedings{zhang-lu-2026-zhangpeng,
    title = "{zhangpeng} at {S}em{E}val-2026 Task 10: {P}sy{C}o{M}ark -- Psycholinguistic Conspiracy Marker Extraction and Detection",
    author = "Zhang, Peng and
        Lu, Gehao",
    booktitle = "Proceedings of the 20th International Workshop on Semantic Evaluation (SemEval-2026)",
    year = "2026",
    month = jul,
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics"
}

@inproceedings{rabehi-etal-2026-macaroni,
    title = "{Team Macaroni} at {S}em{E}val-2026 Task 10: {P}sy{C}o{M}ark -- Psycholinguistic Conspiracy Marker Extraction and Detection",
    author = "Rabehi, Rofa{\"i}da and
        Plenk, Nicolai and
        Han, Sung-Jin Miriam",
    booktitle = "Proceedings of the 20th International Workshop on Semantic Evaluation (SemEval-2026)",
    year = "2026",
    month = jul,
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics"
}

@inproceedings{richter-marreddy-2026-ttlab,
    title = "{TTLab} at {S}em{E}val-2026 Task 10: Transformer-based Approaches for Psycholinguistic Conspiracy Detection in Social Media Discourse",
    author = "Richter, Samuel and
        Marreddy, Mounika",
    booktitle = "Proceedings of the 20th International Workshop on Semantic Evaluation (SemEval-2026)",
    year = "2026",
    month = jul,
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics"
}

@inproceedings{zhu-2026-jia,
    title = "{Jia} at {S}em{E}val-2026 Task 10: A Dual-Track System with {BERT}-based Encoders and {LLM}s for Conspiracy Analysis",
    author = "Zhu, Jiayue",
    booktitle = "Proceedings of the 20th International Workshop on Semantic Evaluation (SemEval-2026)",
    year = "2026",
    month = jul,
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics"
}

@inproceedings{ranxiandong-2026-agai,
    title = "{AGAI} at {S}em{E}val-2026 Task 10: Enhancing Conspiracy Detection via Instruction-tuned {LLM}s",
    author = "Ranxiandong",
    booktitle = "Proceedings of the 20th International Workshop on Semantic Evaluation (SemEval-2026)",
    year = "2026",
    month = jul,
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics"
}

@inproceedings{ziaei-etal-2026-GUNLP,
    title = "{GUNLP} at {S}em{E}val-2026 Task 10: Improving Conspiracy Detection via Emotion-Aware Augmentation",
    author = "Ziaei, Rojin and
        Khoshnoodi, Mahsa and
        Goharian, Nazli",
    booktitle = "Proceedings of the 20th International Workshop on Semantic Evaluation (SemEval-2026)",
    year = "2026",
    month = jul,
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics"
}

@inproceedings{karan-2026-CredenceAI,
    title = "{CredenceAI} at {S}em{E}val-2026 Task 10: A Span-Consistency Network with Cross-Marker Attention for Conspiracy Marker Extraction",
    author = "Karan, Ishaan",
    booktitle = "Proceedings of the 20th International Workshop on Semantic Evaluation (SemEval-2026)",
    year = "2026",
    month = jul,
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics"
}

@inproceedings{fariha-2026-CUET,
    title = "{CUET\_320} at {S}em{E}val-2026 Task 10: Few-Shot Large Language Models for Psycholinguistic Marker Extraction and Conspiracy Detection",
    author = "Fariha, Faozia and 
    Khan, Lamia Tasnim and
    Chowdhury, Madiha Ahmed and
    Ahmed, Kawsar and 
    Hoque, Mohammed Moshiul",
    booktitle = "Proceedings of the 20th International Workshop on Semantic Evaluation (SemEval-2026)",
    year = "2026",
    month = jul,
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics"
}

@inproceedings{chen-2026-YNU,
    title = "{YNU-HPCC} at {S}em{E}val-2026 Task 10: Pretrained DistilBERT Models for Conspiracy Marker Extraction and Detection",
    author = "Chen, Junpei and
    Zhang, You and
    Wang, Jin and
    Xu, Dan and 
    Zhang, Xuejie",
    booktitle = "Proceedings of the 20th International Workshop on Semantic Evaluation (SemEval-2026)",
    year = "2026",
    month = jul,
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics"
}