构建社交平台心理求助数据集，识别用户求救信号与背后原因
M-HELP: Using Social Media Data to Detect Mental Health Help-Seeking Signals
- 构建M-Help数据集，标注求助行为及病因
- 模型可同时识别求助者、病症和根本诱因
- 适合心理健康监测与早期干预研究者使用
精神健康问题已成为全球性危机。尽管已有多种数据集用于检测精神障碍，但对主动寻求帮助的个体识别仍存在关键空白。本文提出新型数据集M-Help，专为在社交媒体上识别心理求助行为而设计。该数据集不仅标注求助行为，还涵盖具体精神障碍类型及其潜在成因，如情感困扰或经济压力。基于M-Help训练的AI模型可完成三项任务：识别求助者、诊断精神健康状况、揭示问题根源。该数据集为心理援助系统提供重要支持。
原文摘要 · Abstract (English)
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.
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