

@article{googlemachinetranslation,
  author={Nidhal, Baccouri},
  url={https://pypi.org/project/deep-translator/#usage},
  title={[{T}he article referred to for machine translation]},
  year={2023},
  journal={pypi}
}          
        

@article{mymodel,
  title={Emotion {C}lassification with {SpaCy} v3 & {C}omet},
  author={Kömeçoğlu, Buluz, Başak},
  year={2023},
  url={https://heartbeat.comet.ml/emotion-classification-with-spacy-v3-comet-eaff310c0d7c}
}          

@article{additionaldata,
  title={Emotion {D}etection in {T}ext using {N}atural {L}anguage {P}rocessing},
  author={Nikam, Sanket},
  year={n.d.},
  url={https://github.com/SannketNikam/Emotion-Detection-in-Text}
}          




@article{o2019social,
  title={The social media propaganda problem is worse than you think},
  author={O’CONNOR, CAILIN and Weatherall, James Owen},
  journal={Issues in Science and Technology},
  volume={36},
  number={1},
  pages={30--32},
  year={2019},
  publisher={JSTOR}
}



@article{aral2019protecting,
  title={Protecting elections from social media manipulation},
  author={Aral, Sinan and Eckles, Dean},
  journal={Science},
  volume={365},
  number={6456},
  pages={858--861},
  year={2019},
  publisher={American Association for the Advancement of Science}
}



@INPROCEEDINGS{9811117,
  author={Abdullah, Malak and Altiti, Ola and Obiedat, Rasha},
  booktitle={2022 13th International Conference on Information and Communication Systems (ICICS)}, 
  title={Detecting Propaganda Techniques in English News Articles using Pre-trained Transformers}, 
  year={2022},
  volume={},
  number={},
  pages={301-308},
  keywords={Analytical models;Social networking (online);Shape;Soft sensors;Psychology;Transformers;Data models;Fake news;propaganda;RoBERTa},
  doi={10.1109/ICICS55353.2022.9811117}}


@article{sprenkamp2023large,
  title={Large Language Models for Propaganda Detection},
  author={Sprenkamp, Kilian and Jones, Daniel Gordon and Zavolokina, Liudmila},
  journal={arXiv preprint arXiv:2310.06422},
  year={2023}
}


@inproceedings{al-omari-etal-2019-justdeep,
    title = "{JUSTD}eep at {NLP}4{IF} 2019 Task 1: Propaganda Detection using Ensemble Deep Learning Models",
    author = "Al-Omari, Hani  and
      Abdullah, Malak  and
      AlTiti, Ola  and
      Shaikh, Samira",
    editor = "Feldman, Anna  and
      Da San Martino, Giovanni  and
      Barr{\'o}n-Cede{\~n}o, Alberto  and
      Brew, Chris  and
      Leberknight, Chris  and
      Nakov, Preslav",
    booktitle = "Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/D19-5016",
    doi = "10.18653/v1/D19-5016",
    pages = "113--118",
    abstract = "The internet and the high use of social media have enabled the modern-day journalism to publish, share and spread news that is difficult to distinguish if it is true or fake. Defining {``}fake news{''} is not well established yet, however, it can be categorized under several labels: false, biased, or framed to mislead the readers that are characterized as propaganda. Digital content production technologies with logical fallacies and emotional language can be used as propaganda techniques to gain more readers or mislead the audience. Recently, several researchers have proposed deep learning (DL) models to address this issue. This research paper provides an ensemble deep learning model using BiLSTM, XGBoost, and BERT to detect propaganda. The proposed model has been applied on the dataset provided by the challenge NLP4IF 2019, Task 1 Sentence Level Classification (SLC) and it shows a significant performance over the baseline model.",
}


@article{kamberi2023overview,
  title={AN OVERVIEW OF THE DISPUTE BETWEEN NORTH MACEDONIA AND BULGARIA THROUGH THE OPTIC OF INTERNATIONAL LAW},
  author={KAMBERI, Donika},
  journal={JUSTICIA--International Journal of Legal Sciences},
  volume={11},
  number={19-20},
  pages={69--75},
  year={2023},
  publisher={University of Tetova}
}



@article{bradshaw2019global,
  title={The global disinformation order: 2019 global inventory of organised social media manipulation},
  author={Bradshaw, Samantha and Howard, Philip N},
  year={2019}
}



@inproceedings{semrel2024task,   
title="{S}em{E}val-2024 {T}ask 4:  {MULTILINGUAL DETECTION OF PERSUASION TECHNIQUES IN MEMES}",  
author="Dimitar Dimitrov and Giovanni Da San Martino and Preslav Nakov and Firoj Alam and Maram Hasanain and Abul Hasnat and Fabrizio Silvestri",   
booktitle="Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)",  
 year=2024
}