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        <title>A Multi-Task Learning Framework for Modeling Engagement and Topic-Sensitive Responses in Arabic Women’s Discourse</title>
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        <namePart type="given">Mabrouka</namePart>
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    <abstract>Predicting how audiences react to Arabic social media posts requires reasoning beyond textual sentiment: reactions emerge from collective interpretation moderated by engagement dynamics and topical context. We present a multi-task learning (MTL) framework that jointly learns (i) audience reaction classification (Love, Haha, Angry, Sad, Care, Wow), (ii) engagement magnitude regression (six reactions, comments, shares), and (iii) non-engagement detection. On a corpus of 158k Arabic Facebook posts spanning women’s rights, gender debates, and economic empowerment, our model achieves a test macro-F1 of 72.4 and weighted-F1 of 89.1.</abstract>
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%0 Conference Proceedings
%T A Multi-Task Learning Framework for Modeling Engagement and Topic-Sensitive Responses in Arabic Women’s Discourse
%A Bessghaier, Mabrouka
%A Biswas, Md. Rafiul
%A Ibrahim, Shimaa
%A Zaghouani, Wajdi
%Y Demberg, Vera
%Y Inui, Kentaro
%Y Marquez, Lluís
%S Findings of the Association for Computational Linguistics: EACL 2026
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-386-9
%F bessghaier-etal-2026-multi
%X Predicting how audiences react to Arabic social media posts requires reasoning beyond textual sentiment: reactions emerge from collective interpretation moderated by engagement dynamics and topical context. We present a multi-task learning (MTL) framework that jointly learns (i) audience reaction classification (Love, Haha, Angry, Sad, Care, Wow), (ii) engagement magnitude regression (six reactions, comments, shares), and (iii) non-engagement detection. On a corpus of 158k Arabic Facebook posts spanning women’s rights, gender debates, and economic empowerment, our model achieves a test macro-F1 of 72.4 and weighted-F1 of 89.1.
%U https://aclanthology.org/2026.findings-eacl.253/
%P 4846-4854

Markdown (Informal)

[A Multi-Task Learning Framework for Modeling Engagement and Topic-Sensitive Responses in Arabic Women’s Discourse](https://aclanthology.org/2026.findings-eacl.253/) (Bessghaier et al., Findings 2026)

• A Multi-Task Learning Framework for Modeling Engagement and Topic-Sensitive Responses in Arabic Women’s Discourse (Bessghaier et al., Findings 2026)

ACL

• Mabrouka Bessghaier, Md. Rafiul Biswas, Shimaa Ibrahim, and Wajdi Zaghouani. 2026. A Multi-Task Learning Framework for Modeling Engagement and Topic-Sensitive Responses in Arabic Women’s Discourse. In Findings of the Association for Computational Linguistics: EACL 2026, pages 4846–4854, Rabat, Morocco. Association for Computational Linguistics.