@inproceedings{labib-etal-2024-cuet,
    title = "{CUET}{\_}sstm at {A}r{AIE}val Shared Task: Unimodal (Text) Propagandistic Technique Detection Using Transformer-Based Model",
    author = "Labib, Momtazul  and
      Rahman, Samia  and
      Murad, Hasan  and
      Das, Udoy",
    editor = "Habash, Nizar  and
      Bouamor, Houda  and
      Eskander, Ramy  and
      Tomeh, Nadi  and
      Abu Farha, Ibrahim  and
      Abdelali, Ahmed  and
      Touileb, Samia  and
      Hamed, Injy  and
      Onaizan, Yaser  and
      Alhafni, Bashar  and
      Antoun, Wissam  and
      Khalifa, Salam  and
      Haddad, Hatem  and
      Zitouni, Imed  and
      AlKhamissi, Badr  and
      Almatham, Rawan  and
      Mrini, Khalil",
    booktitle = "Proceedings of The Second Arabic Natural Language Processing Conference",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2024.arabicnlp-1.52/",
    doi = "10.18653/v1/2024.arabicnlp-1.52",
    pages = "507--511",
    abstract = "In recent days, propaganda has started to influence public opinion increasingly as social media usage continues to grow. Our research has been part of the first challenge, Unimodal (Text) Propagandistic Technique Detection of ArAIEval shared task at the ArabicNLP 2024 conference, co-located with ACL 2024, identifying specific Arabic text spans using twenty-three propaganda techniques. We have augmented underrepresented techniques in the provided dataset using synonym replacement and have evaluated various machine learning (RF, SVM, MNB), deep learning (BiLSTM), and transformer-based models (bert-base-arabic, Marefa-NER, AraBERT) with transfer learning. Our comparative study has shown that the transformer model {\textquotedblleft}bert-base-arabic{\textquotedblright} has outperformed other models. Evaluating the test set, it has achieved the micro-F1 score of 0.2995 which is the highest. This result has secured our team {\textquotedblleft}CUET{\_}sstm{\textquotedblright} first place among all participants in task 1 of the ArAIEval."
}