Malath Al-Sibani


2024

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BiasGanda at FIGNEWS 2024 Shared Task: A Quest to Uncover Biased Views in News Coverage
Al Manar Al Wardi | Blqees Al Busaidi | Malath Al-Sibani | Hiba Salim Muhammad Al-Siyabi | Najma Al Zidjaly
Proceedings of The Second Arabic Natural Language Processing Conference

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.