@inproceedings{chaabane-etal-2026-identifying,
title = "Identifying Political Bias in {A}rabic News Articles",
author = "Chaabane, Saoussen and
Trigui, Omar and
Jaoua, Maher",
editor = "Afli, Haithem and
Bouamor, Houda and
Zaghouani, Wajdi and
Ghannay, Sahar and
Hossain, Shehenaz",
booktitle = "Proceedings of the 3rd Workshop on Natural Language Processing for Political Sciences ({P}olitical{NLP} 2026)",
month = may,
year = "2026",
address = "Palma, Mallorca (Spain)",
publisher = "ELRA Language Resources Association (ELRA)",
url = "https://aclanthology.org/2026.politicalnlp-1.25/",
doi = "10.63317/5a4k3eeanht5",
pages = "228--233",
abstract = "A comprehensive framework was developed to detect political bias in Arabic news articles, with a case study focusing on media reporting of the Palestinian issue. The methodology integrates MARBERT contextual embeddings with classical and deep learning classifiers, including SVM, Logistic Regression, Random Forest, and LSTM. The scalability of data processing was ensured through Apache Spark for potential real-time deployment. Experimental results showed that fine-tuned MARBERT embeddings combined with LSTM achieved the highest classification accuracy of 0.87, along with notable improvements in F1-scores across the pro, against, and neutral categories. These findings highlight the effectiveness of domain-specific fine-tuning of transformer models for political bias classification. The study also addressed class imbalance using SMOTE and class weighting strategies, and assessed feature robustness using multiple vectorization techniques."
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<abstract>A comprehensive framework was developed to detect political bias in Arabic news articles, with a case study focusing on media reporting of the Palestinian issue. The methodology integrates MARBERT contextual embeddings with classical and deep learning classifiers, including SVM, Logistic Regression, Random Forest, and LSTM. The scalability of data processing was ensured through Apache Spark for potential real-time deployment. Experimental results showed that fine-tuned MARBERT embeddings combined with LSTM achieved the highest classification accuracy of 0.87, along with notable improvements in F1-scores across the pro, against, and neutral categories. These findings highlight the effectiveness of domain-specific fine-tuning of transformer models for political bias classification. The study also addressed class imbalance using SMOTE and class weighting strategies, and assessed feature robustness using multiple vectorization techniques.</abstract>
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%0 Conference Proceedings
%T Identifying Political Bias in Arabic News Articles
%A Chaabane, Saoussen
%A Trigui, Omar
%A Jaoua, Maher
%Y Afli, Haithem
%Y Bouamor, Houda
%Y Zaghouani, Wajdi
%Y Ghannay, Sahar
%Y Hossain, Shehenaz
%S Proceedings of the 3rd Workshop on Natural Language Processing for Political Sciences (PoliticalNLP 2026)
%D 2026
%8 May
%I ELRA Language Resources Association (ELRA)
%C Palma, Mallorca (Spain)
%F chaabane-etal-2026-identifying
%X A comprehensive framework was developed to detect political bias in Arabic news articles, with a case study focusing on media reporting of the Palestinian issue. The methodology integrates MARBERT contextual embeddings with classical and deep learning classifiers, including SVM, Logistic Regression, Random Forest, and LSTM. The scalability of data processing was ensured through Apache Spark for potential real-time deployment. Experimental results showed that fine-tuned MARBERT embeddings combined with LSTM achieved the highest classification accuracy of 0.87, along with notable improvements in F1-scores across the pro, against, and neutral categories. These findings highlight the effectiveness of domain-specific fine-tuning of transformer models for political bias classification. The study also addressed class imbalance using SMOTE and class weighting strategies, and assessed feature robustness using multiple vectorization techniques.
%R 10.63317/5a4k3eeanht5
%U https://aclanthology.org/2026.politicalnlp-1.25/
%U https://doi.org/10.63317/5a4k3eeanht5
%P 228-233
Markdown (Informal)
[Identifying Political Bias in Arabic News Articles](https://aclanthology.org/2026.politicalnlp-1.25/) (Chaabane et al., PoliticalNLP 2026)
ACL
- Saoussen Chaabane, Omar Trigui, and Maher Jaoua. 2026. Identifying Political Bias in Arabic News Articles. In Proceedings of the 3rd Workshop on Natural Language Processing for Political Sciences (PoliticalNLP 2026), pages 228–233, Palma, Mallorca (Spain). ELRA Language Resources Association (ELRA).