TY - GEN
T1 - The FIGNEWS Shared Task on News Media Narratives
AU - Zaghouani, Wajdi
AU - Jarrar, Mustafa
AU - Habash, Nizar
AU - Bouamor, Houda
AU - Zitouni, Imed
AU - Diab, Mona
AU - El-Beltagy, Samhaa R.
AU - AbuOdeh, Muhammed
N1 - Publisher Copyright:
©2024 Association for Computational Linguistics.
PY - 2024
Y1 - 2024
N2 - 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.1 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.
AB - 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.1 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.
UR - http://www.scopus.com/inward/record.url?scp=85204309446&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85204309446&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85204309446
T3 - ArabicNLP 2024 - 2nd Arabic Natural Language Processing Conference, Proceedings of the Conference
SP - 530
EP - 547
BT - ArabicNLP 2024 - 2nd Arabic Natural Language Processing Conference, Proceedings of the Conference
A2 - Habash, Nizar
A2 - Bouamor, Houda
A2 - Eskander, Ramy
A2 - Tomeh, Nadi
A2 - Farha, Ibrahim Abu
A2 - Abdelali, Ahmed
A2 - Touileb, Samia
A2 - Hamed, Injy
A2 - Onaizan, Yaser
A2 - Alhafni, Bashar
A2 - Antoun, Wissam
A2 - Khalifa, Salam
A2 - Haddad, Hatem
A2 - Zitouni, Imed
A2 - AlKhamissi, Badr
A2 - Almatham, Rawan
A2 - Mrini, Khalil
PB - Association for Computational Linguistics (ACL)
T2 - 2nd Arabic Natural Language Processing Conference, ArabicNLP 2024
Y2 - 16 August 2024
ER -