M-HELP: Using Social Media Data to Detect Mental Health Help-Seeking Signals
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Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 8 of its 26 references that are in the map. First posted on arXiv 21 Aug 2025.
Builds on
8 of 26 references.
References · 8
- 2022Counseling Summarization using Mental Health Knowledge Guided Utterance Filteringcited by 18
- 2023An Annotated Dataset for Explainable Interpersonal Risk Factors of Mental Disturbance in Social Media Postscited by 16
- 2024Exploring the Efficacy of Large Language Models in Summarizing Mental Health Counseling Sessions: A Benchmark Studycited by 13
- 2024Knowledge Planning in Large Language Models for Domain-Aligned Counseling Summarizationcited by 11
- 2022A Computational Approach to Understand Mental Health from Reddit: Knowledge-aware Multitask Learning Frameworkcited by 8
- 2023Critical Behavioral Traits Foster Peer Engagement in Online Mental Health Communitiescited by 7
- 2025Figurative-cum-Commonsense Knowledge Infusion for Multimodal Mental Health Meme Classificationcited by 6
- 2025Trust Modeling in Counseling Conversations: A Benchmark Studycited by 5
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Citing papers per year: 2026: 3.
3 papers.