@inproceedings{blqees-etal-2024-biasganda,
title = "{B}ias{G}anda at {FIGNEWS} 2024 Shared Task: A Quest to Uncover Biased Views in News Coverage",
author = "Blqees, Blqees and
Wardi, Al and
Al-Sibani, Malath and
Al-Siyabi, Hiba and
Zidjaly, Najma",
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-1.65",
pages = "609--613",
abstract = "In this study, we aimed to identify biased language in a dataset provided by the FIGNEWS 2024 committee on the Gaza-Israel war. We classified entries into seven categories: Unbiased, Biased against Palestine, Biased against Israel, Biased against Others, Biased against both Palestine and Israel, Unclear, and Not Applicable. Our team reviewed the literature to develop a codebook of terminologies and definitions. By coding each example, we sought to detect language tendencies used by media outlets when reporting on the same event. The primary finding was that most examples were classified as {``}Biased against Palestine,{''} as all examined language data used one-sided terms to describe the October 7 event. The least used category was {``}Not Applicable,{''} reserved for irrelevant examples or those lacking context. It is recommended to use neutral and balanced language when reporting volatile political news.",
}
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<abstract>In this study, we aimed to identify biased language in a dataset provided by the FIGNEWS 2024 committee on the Gaza-Israel war. We classified entries into seven categories: Unbiased, Biased against Palestine, Biased against Israel, Biased against Others, Biased against both Palestine and Israel, Unclear, and Not Applicable. Our team reviewed the literature to develop a codebook of terminologies and definitions. By coding each example, we sought to detect language tendencies used by media outlets when reporting on the same event. The primary finding was that most examples were classified as “Biased against Palestine,” as all examined language data used one-sided terms to describe the October 7 event. The least used category was “Not Applicable,” reserved for irrelevant examples or those lacking context. It is recommended to use neutral and balanced language when reporting volatile political news.</abstract>
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<url>https://aclanthology.org/2024.arabicnlp-1.65</url>
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<date>2024-08</date>
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%0 Conference Proceedings
%T BiasGanda at FIGNEWS 2024 Shared Task: A Quest to Uncover Biased Views in News Coverage
%A Blqees, Blqees
%A Wardi, Al
%A Al-Sibani, Malath
%A Al-Siyabi, Hiba
%A Zidjaly, Najma
%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 blqees-etal-2024-biasganda
%X In this study, we aimed to identify biased language in a dataset provided by the FIGNEWS 2024 committee on the Gaza-Israel war. We classified entries into seven categories: Unbiased, Biased against Palestine, Biased against Israel, Biased against Others, Biased against both Palestine and Israel, Unclear, and Not Applicable. Our team reviewed the literature to develop a codebook of terminologies and definitions. By coding each example, we sought to detect language tendencies used by media outlets when reporting on the same event. The primary finding was that most examples were classified as “Biased against Palestine,” as all examined language data used one-sided terms to describe the October 7 event. The least used category was “Not Applicable,” reserved for irrelevant examples or those lacking context. It is recommended to use neutral and balanced language when reporting volatile political news.
%U https://aclanthology.org/2024.arabicnlp-1.65
%P 609-613
Markdown (Informal)
[BiasGanda at FIGNEWS 2024 Shared Task: A Quest to Uncover Biased Views in News Coverage](https://aclanthology.org/2024.arabicnlp-1.65) (Blqees et al., ArabicNLP-WS 2024)
ACL