@inproceedings{zaghouani-etal-2026-posts,
    title = "From Posts to Pressure: An {A}rabic Dataset about Stress and Mental-Health Monitoring",
    author = "Zaghouani, Wajdi  and
      Shlkamy, Eman Sedqy  and
      Bessghaier, Mabrouka",
    booktitle = "Proceedings of the 2nd Workshop on {NLP} for Languages Using {A}rabic Script",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/manual-author-scripts/2026.abjadnlp-1.50/",
    pages = "422--432",
    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."
}