分析用户在Reddit上用AI心理支持的真实故事，发现效果关键在任务匹配而非情感依赖。
Like a Therapist, But Not: Reddit Narratives of AI in Mental Health Contexts
- 用理论框架+人工与大模型结合的方式，分析5126条真实使用案例。
- 任务目标一致时用户评价最积极，陪伴型使用更易引发依赖和症状恶化。
- 适合关注AI心理健康应用的伦理与设计的研究者和从业者。
大型语言模型（LLMs）正越来越多地被用于非临床场景的情感支持与心理健康互动，但人们对这些系统在日常使用中的评价与关系构建仍知之甚少。我们分析了来自47个心理健康社区的5,126条Reddit帖子，内容涉及对AI进行情感支持或治疗的体验与探索。基于技术接受模型与治疗联盟理论，我们构建了一个理论驱动的标注框架，并采用混合式大模型-人工分析流程，在大规模语料中解析评估性语言、采纳态度及关系契合度。结果表明，用户参与主要受叙述性结果、信任度和响应质量影响，而非仅靠情感联结。正向情绪最强烈关联于任务与目标的一致性；而以陪伴为导向的使用则更常出现关系错配，并报告依赖性及症状加重等风险。整体而言，本研究展示了如何将理论构念运用于大规模话语分析，并强调在敏感现实场景中研究用户如何理解语言技术的重要性。
原文摘要 · Abstract (English)
Large language models (LLMs) are increasingly used for emotional support and mental health-related interactions outside clinical settings, yet little is known about how people evaluate and relate to these systems in everyday use. We analyze 5,126 Reddit posts from 47 mental health communities describing experiential or exploratory use of AI for emotional support or therapy. Grounded in the Technology Acceptance Model and therapeutic alliance theory, we develop a theory-informed annotation framework and apply a hybrid LLM-human pipeline to analyze evaluative language, adoption-related attitudes, and relational alignment at scale. Our results show that engagement is shaped primarily by narrated outcomes, trust, and response quality, rather than emotional bond alone. Positive sentiment is most strongly associated with task and goal alignment, while companionship-oriented use more often involves misaligned alliances and reported risks such as dependence and symptom escalation. Overall, this work demonstrates how theory-grounded constructs can be operationalized in large-scale discourse analysis and highlights the importance of studying how users interpret language technologies in sensitive, real-world contexts.
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