Uot1 at FIGNEWS 2024 Shared Task: Labeling News Bias

Abdusalam Nwesri, Mai Elbaabaa, Fatima Lashihar, Fatma Alalos

Abstract
This paper outlines the University of Tripoli’s initiative in creating annotation guidelines to detect bias in news articles concerning the Palestinian-Israeli conflict. Our team participated in the Framing of Israeli Gaza News Media Narrative (FIGNEWS 2024) shared task. We developed annotation guidelines to label bias in news articles. Using those guidelines we managed to annotate 3,900 articles with the aid of our custom-developed annotation tool. Among 16 participating teams, we scored 48.7 on the macro F1 measure in the quality track in which we ranked 4th. In the centrality track we were ranked at the 6th position using the macro F1 avg measure, however, we achieved the 4th best kappa coefficient. Our bias annotation guidelines was ranked in the 9th position.
Anthology ID:
2024.arabicnlp-1.60
Volume:
Proceedings of The Second Arabic Natural Language Processing Conference
Month:
August
Year:
2024
Address:
Bangkok, Thailand
Editors:
Nizar Habash, Houda Bouamor, Ramy Eskander, Nadi Tomeh, Ibrahim Abu Farha, Ahmed Abdelali, Samia Touileb, Injy Hamed, Yaser Onaizan, Bashar Alhafni, Wissam Antoun, Salam Khalifa, Hatem Haddad, Imed Zitouni, Badr AlKhamissi, Rawan Almatham, Khalil Mrini
Venues:
ArabicNLP | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
567–572
Language:
URL:
https://aclanthology.org/2024.arabicnlp-1.60
DOI:
10.18653/v1/2024.arabicnlp-1.60
Bibkey:
Cite (ACL):
Abdusalam Nwesri, Mai Elbaabaa, Fatima Lashihar, and Fatma Alalos. 2024. Uot1 at FIGNEWS 2024 Shared Task: Labeling News Bias. In Proceedings of The Second Arabic Natural Language Processing Conference, pages 567–572, Bangkok, Thailand. Association for Computational Linguistics.
Cite (Informal):
Uot1 at FIGNEWS 2024 Shared Task: Labeling News Bias (Nwesri et al., ArabicNLP-WS 2024)
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PDF:
https://preview.aclanthology.org/dois-2013-emnlp/2024.arabicnlp-1.60.pdf
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