ArAIEval Shared Task: Propagandistic Techniques Detection in Unimodal and Multimodal Arabic Content

TL;DR

The paper reports on the second ArAIEval shared task focusing on detecting propagandistic techniques in Arabic textual and multimodal content, highlighting system performances and dataset sharing to advance research.

Contribution

It introduces two new tasks for propagandistic content detection in Arabic, with detailed dataset construction, evaluation setup, and participation analysis, emphasizing transformer-based models.

Findings

Transformer models like AraBERT are central to most systems.

High participation indicates strong community interest.

Datasets and scripts are publicly released for future research.

Abstract

We present an overview of the second edition of the ArAIEval shared task, organized as part of the ArabicNLP 2024 conference co-located with ACL 2024. In this edition, ArAIEval offers two tasks: (i) detection of propagandistic textual spans with persuasion techniques identification in tweets and news articles, and (ii) distinguishing between propagandistic and non-propagandistic memes. A total of 14 teams participated in the final evaluation phase, with 6 and 9 teams participating in Tasks 1 and 2, respectively. Finally, 11 teams submitted system description papers. 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 provide a brief overview of the participating systems. All datasets and evaluation scripts are released to the research community (https://araieval.gitlab.io/). We hope this will enable further research on these important tasks in Arabic.