M -Help: Using Social Media Data to Detect Mental Health Help-Seeking Signals (2025)

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.

## introduction mental health challenges such as anxiety, depression, and suicidal thoughts are global concerns. despite growing awareness, stigma and the complexity of navigating mental health care systems create significant barriers to seeking help @xcite . these challenges are exacerbated by a lack of affordable services in underserved areas @xcite and limited guidance on accessing resources @xcite . although awareness campaigns reduce stigma, many remain unsure where to start, leading to delays or avoidance in seeking help @xcite . platforms like reddit provide anonymous emotional support but lack the professional intervention needed for long-term recovery. while useful as a starting point, they cannot replace trained therapists or structured care. this underscores the need for solutions integrating online resources with accessible services like virtual therapy. a critical question arises: can ai-powered platforms, leveraging advanced technologies like llms, effectively detect and support individuals facing mental or financial challenges? to address this question, we propose a system that leverages social media data to identify individuals exhibiting help-seeking behavior. by developing a novel dataset m-help foot_0 focused on detecting specific mental health conditions and their underlying causes, we aim to enable ngos, government organizations, and other stakeholders to engage with at-risk individuals more effectively, offering personalized support and counseling. prior research has laid a strong foundation in this space by introducing datasets and models for mental health disorder detection. for instance, @xcite @xcite @xcite developed innovative datasets and machine learning models to detect mental health disorders. these systems leverage natural language processing (nlp) to analyze social media data, such as posts and interactions on platforms like reddit and twitter, to identify mental health issues through users' online behavior. similarly, @xcite @xcite created datasets and nlp models that summarize counseling sessions, emphasizing actionable feedback to improve therapy effectiveness and assist mental health professionals in planning interventions. additionally, @xcite @xcite introduced explainable mental health detection datasets, focusing on identifying disorders and their underlying causes. by incorporating cause-related features, their work offers insights into users' mental states and circumstances. existing datasets and models have advanced mental health detection @xcite , but their focus remains largely on disorder identification and counseling analysis and summarization @xcite , leaving a critical gap in identifying and supporting individuals actively seeking help for mental and financial issues. our work bridges this gap by introducing a novel dataset specifically designed to detect helpseeking behavior, its underlying causes, and mental health disorders on social media, making it a holistic resource. our contributions are as follows: we introduce the first dataset focused on detecting help-seeking behavior on social media. 2. multi-label mental health disorder classification: we also incorporate multi-label classification data along with their underlying causes for the disorder. 3. evaluation of sota models: we evaluated advanced models such as gpt-4, gemini-1.5, and llama 3.1, proving their effectiveness in detecting help-seeking behavior. we also release the m-help dataset and code to support psychologists, ngos, and policymakers in connecting individuals with necessary resources. ## experimental details baselines. we evaluated a range of baseline models for disorder classification and help-seeking classification, covering encoder-only, long-context, encoder-decoder, decoder-only, and closed-source architectures. performance metrics, including accuracy, macro f1, weighted f1, and micro f1 for multi-label classification, are presented in ## conclusion and future work in this paper, we introduce m-help, a novel dataset designed to identify help-seeking posts related to mental health issues and their underlying causes. unlike traditional datasets, m-help uniquely focuses on capturing active helpseeking behavior, addressing a critical gap in mental health research. we benchmark state-of-the-art (sota) models, including gpt-4o, llama-3.1, and other leading transformer-based architectures, to evaluate the dataset's effectiveness. additionally, we propose a multi-label disorder classification framework that not only identifies mental health disorders but also determines the underlying causes of distress. this work fills a crucial void in the mental health research community, providing a structured benchmark for identifying distress cues where timely intervention is needed. by expanding m-help to support multiple languages and incorporating advanced detection techniques, future research can significantly enhance its effectiveness, fostering a more inclusive and impactful global mental health support system.