@inproceedings{azizov-etal-2023-frank,
    title = "Frank at {A}r{AIE}val Shared Task: {A}rabic Persuasion and Disinformation: The Power of Pretrained Models",
    author = "Azizov, Dilshod  and
      Li, Jiyong  and
      Liang, Shangsong",
    editor = "Sawaf, Hassan  and
      El-Beltagy, Samhaa  and
      Zaghouani, Wajdi  and
      Magdy, Walid  and
      Abdelali, Ahmed  and
      Tomeh, Nadi  and
      Abu Farha, Ibrahim  and
      Habash, Nizar  and
      Khalifa, Salam  and
      Keleg, Amr  and
      Haddad, Hatem  and
      Zitouni, Imed  and
      Mrini, Khalil  and
      Almatham, Rawan",
    booktitle = "Proceedings of ArabicNLP 2023",
    month = dec,
    year = "2023",
    address = "Singapore (Hybrid)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2023.arabicnlp-1.59/",
    doi = "10.18653/v1/2023.arabicnlp-1.59",
    pages = "583--588",
    abstract = {In this work, we present our systems developed for ``ArAIEval'' shared task of ArabicNLP 2023 (CITATION). We used an mBERT transformer for Subtask 1A, which targets persuasion in Arabic tweets, and we used the MARBERT transformer for Subtask 2A to identify disinformation in Arabic tweets. Our persuasion detection system achieved micro-F1 of \textbf{0.745} by surpassing the baseline by 13.2{\%}, and registered a macro-F1 of 0.717 based on leaderboard scores. Similarly, our disinformation system recorded a micro-F1 of \textbf{0.816}, besting the na{\"i}ve majority by 6.7{\%}, with a macro-F1 of 0.637. Furthermore, we present our preliminary results on a variety of pre-trained models. In terms of overall ranking, our systems placed $7^\text{th}$ out of 16 and $12^\text{th}$ out of 17 teams for Subtasks 1A and 2A, respectively.}
}