# M-HELP: Using Social Media Data to Detect Mental Health Help-Seeking Signals

arXiv: https://arxiv.org/abs/2508.15440 · HTML: https://mlsanity.com/paper/2508.15440/

## 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 (most cited first, 8 of 8)

- [Counseling Summarization using Mental Health Knowledge Guided Utterance Filtering](https://mlsanity.com/paper/2206.03886/) (2022)
- [An Annotated Dataset for Explainable Interpersonal Risk Factors of Mental Disturbance in Social Media Posts](https://mlsanity.com/paper/2305.18727/) (2023)
- [Exploring the Efficacy of Large Language Models in Summarizing Mental Health Counseling Sessions: A Benchmark Study](https://mlsanity.com/paper/2402.19052/) (2024)
- [Knowledge Planning in Large Language Models for Domain-Aligned Counseling Summarization](https://mlsanity.com/paper/2409.14907/) (2024)
- [A Computational Approach to Understand Mental Health from Reddit: Knowledge-aware Multitask Learning Framework](https://mlsanity.com/paper/2203.11856/) (2022)
- [Critical Behavioral Traits Foster Peer Engagement in Online Mental Health Communities](https://mlsanity.com/paper/2309.01618/) (2023)
- [Figurative-cum-Commonsense Knowledge Infusion for Multimodal Mental Health Meme Classification](https://mlsanity.com/paper/2501.15321/) (2025)
- [Trust Modeling in Counseling Conversations: A Benchmark Study](https://mlsanity.com/paper/2501.03064/) (2025)

## Built on by (most cited first, 3 of 3)

- [Responsible Evaluation of AI for Mental Health](https://mlsanity.com/paper/2602.00065/) (2026)
- [MMHBench: A Multi-Perspective Benchmark for Mental Health Understanding in Long-Form Videos](https://mlsanity.com/paper/2607.27895/) (2026)
- [Can Large Language Models Resolve Semantic Discrepancy in Self-Destructive Subcultures? Evidence from Jirai Kei](https://mlsanity.com/paper/2601.05004/) (2026)

## 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.