Mai Elbaabaa


2024

pdf bib
Uot1 at FIGNEWS 2024 Shared Task: Labeling News Bias
Abdusalam Nwesri | Mai Elbaabaa | Fatima Lashihar | Fatma Alalos
Proceedings of The Second Arabic Natural Language Processing Conference

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.