@inproceedings{amini-kosseim-2025-posts,
    title = "From Posts to Predictions: A User-Aware Framework for Faithful and Transparent Detection of Mental Health Risks on Social Media",
    author = "Amini, Hessam  and
      Kosseim, Leila",
    editor = "Angelova, Galia  and
      Kunilovskaya, Maria  and
      Escribe, Marie  and
      Mitkov, Ruslan",
    booktitle = "Proceedings of the 15th International Conference on Recent Advances in Natural Language Processing - Natural Language Processing in the Generative AI Era",
    month = sep,
    year = "2025",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd., Shoumen, Bulgaria",
    url = "https://preview.aclanthology.org/corrections-2026-01/2025.ranlp-1.9/",
    pages = "75--84",
    abstract = "We propose a user-aware attention-based framework for early detection of mental health risks from social media posts. Our model combines DisorBERT, a mental health{--}adapted transformer encoder, with a user-level attention mechanism that produces transparent post-level explanations. To assess whether these explanations are faithful, i.e., aligned with the model{'}s true decision process, we apply adversarial training and quantify attention faithfulness using the AtteFa metric. Experiments on four eRisk tasks (depression, anorexia, self-harm, and pathological gambling) show that our model achieves competitive latency-weighted F1 scores while relying on a sparse subset of posts per user. We also evaluate attention robustness and conduct ablations, confirming the model{'}s reliance on high-weighted posts. Our work extends prior explainability studies by integrating faithfulness assessment in a real-world high-stakes application. We argue that systems combining predictive accuracy with faithful and transparent explanations offer a promising path toward safe and trustworthy AI for mental health support."
}