@inproceedings{bessghaier-etal-2026-multi,
    title = "A Multi-Task Learning Framework for Modeling Engagement and Topic-Sensitive Responses in {A}rabic Women{'}s Discourse",
    author = "Bessghaier, Mabrouka  and
      Biswas, Md. Rafiul  and
      Ibrahim, Shimaa  and
      Zaghouani, Wajdi",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EACL} 2026",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.findings-eacl.253/",
    pages = "4846--4854",
    ISBN = "979-8-89176-386-9",
    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."
}