分析18.7万条ChatGPT对话，发现抑郁症状者更常深夜倾诉、反复求助。
Depression Symptoms and Relational Patterns in 187k ChatGPT Histories
- 对比PHQ-8评分高低群体，分析其与ChatGPT的对话模式差异
- 高分组在心理、人际、孤独话题上使用更多，且有明显夜间和月周期行为
- 语言含更多第一人称和绝对词，但无法有效用于抑郁筛查
大型语言模型正日益成为私密、随时可用的对话系统，但关于有抑郁症状者如何使用它们的研究仍很少。基于披露与同伴支持的CSCW研究，我们把ChatGPT视为一种新兴的非正式支持基础设施：私密、持久、响应迅速，且可在常规时间外使用。我们分析了766名完成PHQ-8量表的参与者共187,093条ChatGPT对话，比较得分低于10（轻度）与等于或高于10（中度及以上）的群体。高PHQ-8分者更频繁地与ChatGPT讨论心理健康、人际关系、孤独感、自我关注及寻求支持话题，并表现出显著的深夜使用和按月重复的模式。其语言中第一人称单数代词和绝对化表达更多。他们在高披露场景中更活跃，但未见更高频率的专业引导。基于语言的预测效果有限，筛选性能不高（AUROC 0.591）。我们认为这些对话历史不应被视为临床筛查数据，而应作为证据表明大模型正被广泛用作非正式支持基础设施。
原文摘要 · Abstract (English)
Large language models are increasingly used as private, always-available conversational systems, but little is known about how people with depressive symptoms use them. Building on CSCW work on disclosure and peer support, we examine ChatGPT as an emerging informal support infrastructure: private, persistent, responsive, and available outside ordinary hours. We analyze 187,093 ChatGPT conversations from 766 participants who completed the PHQ-8, comparing those below the moderate-symptom threshold (score of 10) with those at or above it. Higher-PHQ participants used ChatGPT more for mental-health, interpersonal, loneliness, self-focused, and support-seeking conversations, with pronounced late-night and recurring month-level patterns. Their language contained more first-person singular pronouns and absolutist terms. They more often engaged ChatGPT in high-disclosure contexts, but professional redirection was not higher. Language-based prediction was modest and insufficient for screening (AUROC 0.591). We argue these histories should not be treated as clinical screening data but as evidence LLMs are increasingly used as informal support infrastructure.
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