We present an overview of the ArAIEval shared task, organized as part of the first ArabicNLP 2023 conference co-located with EMNLP 2023. ArAIEval offers two tasks over Arabic text: (i) persuasion technique detection, focusing on identifying persuasion techniques in tweets and news articles, and (ii) disinformation detection in binary and multiclass setups over tweets. A total of 20 teams participated in the final evaluation phase, with 14 and 16 teams participating in Tasks 1 and 2, respectively. Across both tasks, we observed that fine-tuning transformer models such as AraBERT was at the core of the majority of the participating systems. We provide a description of the task setup, including a description of the dataset construction and the evaluation setup. We further give a brief overview of the participating systems. All datasets and evaluation scripts from the shared task are released to the research community. (https://araieval.gitlab.io/) We hope this will enable further research on these important tasks in Arabic.
@article{arxiv.2311.03179,
title = {ArAIEval Shared Task: Persuasion Techniques and Disinformation Detection in Arabic Text},
author = {Maram Hasanain and Firoj Alam and Hamdy Mubarak and Samir Abdaljalil and Wajdi Zaghouani and Preslav Nakov and Giovanni Da San Martino and Abed Alhakim Freihat},
journal= {arXiv preprint arXiv:2311.03179},
year = {2023}
}
Comments
Accepted at ArabicNLP-23 (EMNLP-23), propaganda, disinformation, misinformation, fake news