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        <title>From Posts to Pressure: An Arabic Dataset about Stress and Mental-Health Monitoring</title>
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        <namePart type="given">Eman</namePart>
        <namePart type="given">Sedqy</namePart>
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            <title>Proceedings of the 2nd Workshop on NLP for Languages Using Arabic Script</title>
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    <abstract>How do Arabic-speaking communities express and engage with psychological stress on social media? We introduce AraStress, the first large-scale Arabic corpus dedicated to psychological stress research, comprising 175,862 public social media posts from 2020 to 2024, covering pandemic and post-pandemic periods.It fills a significant gap in Arabic mental-health NLP resources focused on stress, enabling large-scale analysis of related expressions.Unlike prior work focusing primarily on Twitter and depression or suicidality, AraStress addresses the critical gap in stress-focused resources. Our lexicon-based analysis reveals that stress-related posts elicit predominantly affective engagement and exhibit a hybrid lexical framing that integrates religious and therapeutic language. AraStress provides a foundational resource for culturally grounded computational models of stress detection and digital wellbeing in Arabic-speaking communities.</abstract>
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%0 Conference Proceedings
%T From Posts to Pressure: An Arabic Dataset about Stress and Mental-Health Monitoring
%A Zaghouani, Wajdi
%A Shlkamy, Eman Sedqy
%A Bessghaier, Mabrouka
%S Proceedings of the 2nd Workshop on NLP for Languages Using Arabic Script
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%F zaghouani-etal-2026-posts
%X How do Arabic-speaking communities express and engage with psychological stress on social media? We introduce AraStress, the first large-scale Arabic corpus dedicated to psychological stress research, comprising 175,862 public social media posts from 2020 to 2024, covering pandemic and post-pandemic periods.It fills a significant gap in Arabic mental-health NLP resources focused on stress, enabling large-scale analysis of related expressions.Unlike prior work focusing primarily on Twitter and depression or suicidality, AraStress addresses the critical gap in stress-focused resources. Our lexicon-based analysis reveals that stress-related posts elicit predominantly affective engagement and exhibit a hybrid lexical framing that integrates religious and therapeutic language. AraStress provides a foundational resource for culturally grounded computational models of stress detection and digital wellbeing in Arabic-speaking communities.
%U https://aclanthology.org/2026.abjadnlp-1.50/
%P 422-432

Markdown (Informal)

[From Posts to Pressure: An Arabic Dataset about Stress and Mental-Health Monitoring](https://aclanthology.org/2026.abjadnlp-1.50/) (Zaghouani et al., AbjadNLP 2026)

• From Posts to Pressure: An Arabic Dataset about Stress and Mental-Health Monitoring (Zaghouani et al., AbjadNLP 2026)

ACL

• Wajdi Zaghouani, Eman Sedqy Shlkamy, and Mabrouka Bessghaier. 2026. From Posts to Pressure: An Arabic Dataset about Stress and Mental-Health Monitoring. In Proceedings of the 2nd Workshop on NLP for Languages Using Arabic Script, pages 422–432, Rabat, Morocco. Association for Computational Linguistics.