@inproceedings{mohammed-etal-2025-bias,
title = "Bias Detection in Media: Traditional Models vs. Transformers in Analyzing Social Media Coverage of the Israeli-{G}aza Conflict",
author = "Mohammed, Marryam Yahya and
Mohamed, Esraa Ismail and
Esmat, Mariam Nabil and
Nagib, Yomna Ashraf and
Radwan, Nada Ahmed and
Elshaer, Ziad Mohamed and
Mohamed, Ensaf Hussein",
editor = "Jarrar, Mustafa and
Habash, Habash and
El-Haj, Mo",
booktitle = "Proceedings of the first International Workshop on Nakba Narratives as Language Resources",
month = jan,
year = "2025",
address = "Abu Dhabi",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.nakbanlp-1.12/",
pages = "114--121",
abstract = "Bias in news reporting significantly influences public perception, particularly in sensitive and polarized contexts like the Israel-Gaza conflict. Detecting bias in such cases presents unique challenges due to political, cultural, and ideological complexities, often amplifying disparities in reporting. While prior research has addressed media bias and dataset fairness, these approaches inadequately capture the nuanced dynamics of the Israel-Gaza conflict. To address this gap, we propose an NLP-based framework that leverages Nakba narratives as linguistic resources for bias detection in news coverage. Using a multilingual corpus focusing on Arabic texts, we apply rigorous data cleaning, pre-processing, and methods to mitigate imbalanced class distributions that could skew classification outcomes. Our study explores various approaches, including Machine Learning (ML), Deep Learning (DL), Transformer-based architectures, and generative models. The findings demonstrate promising advancements in automating bias detection, and enhancing fairness and accuracy in politically sensitive reporting."
}
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<abstract>Bias in news reporting significantly influences public perception, particularly in sensitive and polarized contexts like the Israel-Gaza conflict. Detecting bias in such cases presents unique challenges due to political, cultural, and ideological complexities, often amplifying disparities in reporting. While prior research has addressed media bias and dataset fairness, these approaches inadequately capture the nuanced dynamics of the Israel-Gaza conflict. To address this gap, we propose an NLP-based framework that leverages Nakba narratives as linguistic resources for bias detection in news coverage. Using a multilingual corpus focusing on Arabic texts, we apply rigorous data cleaning, pre-processing, and methods to mitigate imbalanced class distributions that could skew classification outcomes. Our study explores various approaches, including Machine Learning (ML), Deep Learning (DL), Transformer-based architectures, and generative models. The findings demonstrate promising advancements in automating bias detection, and enhancing fairness and accuracy in politically sensitive reporting.</abstract>
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%0 Conference Proceedings
%T Bias Detection in Media: Traditional Models vs. Transformers in Analyzing Social Media Coverage of the Israeli-Gaza Conflict
%A Mohammed, Marryam Yahya
%A Mohamed, Esraa Ismail
%A Esmat, Mariam Nabil
%A Nagib, Yomna Ashraf
%A Radwan, Nada Ahmed
%A Elshaer, Ziad Mohamed
%A Mohamed, Ensaf Hussein
%Y Jarrar, Mustafa
%Y Habash, Habash
%Y El-Haj, Mo
%S Proceedings of the first International Workshop on Nakba Narratives as Language Resources
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi
%F mohammed-etal-2025-bias
%X Bias in news reporting significantly influences public perception, particularly in sensitive and polarized contexts like the Israel-Gaza conflict. Detecting bias in such cases presents unique challenges due to political, cultural, and ideological complexities, often amplifying disparities in reporting. While prior research has addressed media bias and dataset fairness, these approaches inadequately capture the nuanced dynamics of the Israel-Gaza conflict. To address this gap, we propose an NLP-based framework that leverages Nakba narratives as linguistic resources for bias detection in news coverage. Using a multilingual corpus focusing on Arabic texts, we apply rigorous data cleaning, pre-processing, and methods to mitigate imbalanced class distributions that could skew classification outcomes. Our study explores various approaches, including Machine Learning (ML), Deep Learning (DL), Transformer-based architectures, and generative models. The findings demonstrate promising advancements in automating bias detection, and enhancing fairness and accuracy in politically sensitive reporting.
%U https://aclanthology.org/2025.nakbanlp-1.12/
%P 114-121
Markdown (Informal)
[Bias Detection in Media: Traditional Models vs. Transformers in Analyzing Social Media Coverage of the Israeli-Gaza Conflict](https://aclanthology.org/2025.nakbanlp-1.12/) (Mohammed et al., NakbaNLP 2025)
ACL