@inproceedings{sathvik-etal-2025-help,
    title = "{M}-Help: Using Social Media Data to Detect Mental Health Help-Seeking Signals",
    author = "Sathvik, Msvpj  and
      Shaik, Zuhair Hasan  and
      Gupta, Vivek",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.1225/",
    doi = "10.18653/v1/2025.findings-emnlp.1225",
    pages = "22510--22520",
    ISBN = "979-8-89176-335-7",
    abstract = "Mental health disorders are a global crisis. While various datasets exist for detecting such disorders, there remains a critical gap in identifying individuals actively seeking help. This paper introduces a novel dataset, M-Help, specifically designed to detect help-seeking behavior on social media. The dataset goes beyond traditional labels by identifying not only help-seeking activity but also specific mental health disorders and their underlying causes, such as relationship challenges or financial stressors. AI models trained on M-Help can address three key tasks: identifying help-seekers, diagnosing mental health conditions, and uncovering the root causes of issues."
}