The FIGNEWS Shared Task on News Media Narratives
Zaghouani, Wajdi, Jarrar, Mustafa, Habash, Nizar, Bouamor, Houda, Zitouni, Imed, Diab, Mona, El-Beltagy, Samhaa R., AbuOdeh, Muhammed
–arXiv.org Artificial Intelligence
We present an overview of the FIGNEWS shared task, organized as part of the ArabicNLP 2024 conference co-located with ACL 2024. The shared task addresses bias and propaganda annotation in multilingual news posts. We focus on the early days of the Israel War on Gaza as a case study. The task aims to foster collaboration in developing annotation guidelines for subjective tasks by creating frameworks for analyzing diverse narratives highlighting potential bias and propaganda. In a spirit of fostering and encouraging diversity, we address the problem from a multilingual perspective, namely within five languages: English, French, Arabic, Hebrew, and Hindi. A total of 17 teams participated in two annotation subtasks: bias (16 teams) and propaganda (6 teams). The teams competed in four evaluation tracks: guidelines development, annotation quality, annotation quantity, and consistency. Collectively, the teams produced 129,800 data points. Key findings and implications for the field are discussed.
arXiv.org Artificial Intelligence
Jul-25-2024
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