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

2025/08/21 by MSVPJ Sathvik, Sathvik, MSVPJ, Zuhair Hasan Shaik +3 · 2 citations
Computer Science · Psychology · #Computation and Language (cs.CL) #Digital Mental Health Interventions #FOS: Computer and information sciences #Mental Health via Writing #Sentiment Analysis and Opinion Mining

paper · pdf · doi:10.48550/arxiv.2508.15440

openalex publication_date 2025/08/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• Figurative-cum-Commonsense Knowledge Infusion for Multimodal Mental Health Meme Classification
• Trust Modeling in Counseling Conversations: A Benchmark Study
• Knowledge Planning in Large Language Models for Domain-Aligned Counseling Summarization
• Exploring the Efficacy of Large Language Models in Summarizing Mental Health Counseling Sessions: A Benchmark Study
• Critical Behavioral Traits Foster Peer Engagement in Online Mental Health Communities
• An Annotated Dataset for Explainable Interpersonal Risk Factors of Mental Disturbance in Social Media Posts
• Counseling Summarization using Mental Health Knowledge Guided Utterance Filtering
• A Computational Approach to Understand Mental Health from Reddit: Knowledge-aware Multitask Learning Framework

Cited by

• MMHBench: A Multi-Perspective Benchmark for Mental Health Understanding in Long-Form Videos
• Can Large Language Models Resolve Semantic Discrepancy in Self-Destructive Subcultures? Evidence from Jirai Kei

Related

• Mental Illness Classification on Social Media Texts using Deep Learning and Transfer Learning
• Explainable AI for Mental Disorder Detection via Social Media: A survey and outlook
• Tutorial on Using Machine Learning and Deep Learning Models for Mental Illness Detection
• Predicting mental health using social media: A roadmap for future development
• Social Media for Mental Health: Data, Methods, and Findings