@inproceedings{nwesri-etal-2024-uot1,
title = "Uot1 at {FIGNEWS} 2024 Shared Task: Labeling News Bias",
author = "Nwesri, Abdusalam and
Elbaabaa, Mai and
Lashihar, Fatima and
Alalos, Fatma",
editor = "Habash, Nizar and
Bouamor, Houda and
Eskander, Ramy and
Tomeh, Nadi and
Abu Farha, Ibrahim and
Abdelali, Ahmed and
Touileb, Samia and
Hamed, Injy and
Onaizan, Yaser and
Alhafni, Bashar and
Antoun, Wissam and
Khalifa, Salam and
Haddad, Hatem and
Zitouni, Imed and
AlKhamissi, Badr and
Almatham, Rawan and
Mrini, Khalil",
booktitle = "Proceedings of The Second Arabic Natural Language Processing Conference",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.arabicnlp-1.60",
pages = "567--572",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Uot1 at FIGNEWS 2024 Shared Task: Labeling News Bias
%A Nwesri, Abdusalam
%A Elbaabaa, Mai
%A Lashihar, Fatima
%A Alalos, Fatma
%Y Habash, Nizar
%Y Bouamor, Houda
%Y Eskander, Ramy
%Y Tomeh, Nadi
%Y Abu Farha, Ibrahim
%Y Abdelali, Ahmed
%Y Touileb, Samia
%Y Hamed, Injy
%Y Onaizan, Yaser
%Y Alhafni, Bashar
%Y Antoun, Wissam
%Y Khalifa, Salam
%Y Haddad, Hatem
%Y Zitouni, Imed
%Y AlKhamissi, Badr
%Y Almatham, Rawan
%Y Mrini, Khalil
%S Proceedings of The Second Arabic Natural Language Processing Conference
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F nwesri-etal-2024-uot1
%X 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.
%U https://aclanthology.org/2024.arabicnlp-1.60
%P 567-572
Markdown (Informal)
[Uot1 at FIGNEWS 2024 Shared Task: Labeling News Bias](https://aclanthology.org/2024.arabicnlp-1.60) (Nwesri et al., ArabicNLP-WS 2024)
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.