@inproceedings{refaee-etal-2022-arabem,
    title = "{A}ra{BEM} at {WANLP} 2022 Shared Task: Propaganda Detection in {A}rabic Tweets",
    author = "Refaee, Eshrag Ali  and
      Ahmed, Basem  and
      Saad, Motaz",
    editor = "Bouamor, Houda  and
      Al-Khalifa, Hend  and
      Darwish, Kareem  and
      Rambow, Owen  and
      Bougares, Fethi  and
      Abdelali, Ahmed  and
      Tomeh, Nadi  and
      Khalifa, Salam  and
      Zaghouani, Wajdi",
    booktitle = "Proceedings of the Seventh Arabic Natural Language Processing Workshop (WANLP)",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates (Hybrid)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2022.wanlp-1.62/",
    doi = "10.18653/v1/2022.wanlp-1.62",
    pages = "524--528",
    abstract = "Propaganda is information or ideas that an organized group or government spreads to influence people{\'s} opinions, especially by not giving all the facts or secretly emphasizing only one way of looking at the points. The ability to automatically detect propaganda-related linguistic signs is a challenging task that researchers in the NLP community have recently started to address. This paper presents the participation of our team AraBEM in the propaganda detection shared task on Arabic tweets. Our system utilized a pre-trained BERT model to perform multi-class binary classification. It attained the best score at 0.602 micro-f1, ranking third on subtask-1, which identifies the propaganda techniques as a multilabel classification problem with a baseline of 0.079."
}