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Learning to Engage: Modeling Topic-Sensitive Reactions in Arabic Women’s Online Discourse

  • Mabrouka Bessghaier
  • , Md R. Biswas
  • , Shimaa Amer Ibrahim
  • , Wajdi Zaghouani
  • Northwestern University in Qatar

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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 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 macroF1 of 72.4 and weighted-F1 of 89.1.

Original languageEnglish
Title of host publication19th Conference of the European Chapter of the Association for Computational Linguistics, Findings of EACL 2026
PublisherAssociation for Computational Linguistics (ACL)
Pages4846-4854
Number of pages9
ISBN (Electronic)9798891763869
DOIs
Publication statusPublished - 29 Mar 2026
Event19th Conference of the European Chapter of the Association for Computational Linguistics, Findings of EACL 2026 - Rabat, Morocco
Duration: 24 Mar 202629 Mar 2026

Publication series

Name19th Conference of the European Chapter of the Association for Computational Linguistics, Findings of EACL 2026

Conference

Conference19th Conference of the European Chapter of the Association for Computational Linguistics, Findings of EACL 2026
Country/TerritoryMorocco
CityRabat
Period24/03/2629/03/26

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