"I've talked to ChatGPT about my issues last night.": Examining Mental Health Conversations with Large Language Models through Reddit Analysis
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Abstract (arXiv)
We investigate the role of large language models (LLMs) in supporting mental health by analyzing Reddit posts and comments about mental health conversations with ChatGPT. Our findings reveal that users value ChatGPT as a safe, non-judgmental space, often favoring it over human support due to its accessibility, availability, and knowledgeable responses. ChatGPT provides a range of support, including actionable advice, emotional support, and validation, while helping users better understand their mental states. Additionally, we found that ChatGPT offers innovative support for individuals facing mental health challenges, such as assistance in navigating difficult conversations, preparing for therapy sessions, and exploring therapeutic interventions. However, users also voiced potential risks, including the spread of incorrect health advice, ChatGPT's overly validating nature, and privacy concerns. We discuss the implications of LLMs as tools for mental health support in both everyday health and clinical therapy settings and suggest strategies to mitigate risks in LLM-powered interactions.
Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). First posted on arXiv 29 Apr 2025.
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Citing papers per year: 2025: 2; 2026: 6.
8 papers.
- 2025Mental Models of Autonomy and Sentience Shape Reactions to AIcited by 3
- 2025Empathy-R1: A Chain-of-Empathy and Reinforcement Learning Framework for Long-Form Mental Health Supportcited by 2
- 2026AI and My Values: User Perceptions of LLMs' Ability to Extract, Embody, and Explain Human Values from Casual Conversationscited by 2
- 2026Polite But Boring? Trade-offs Between Engagement and Psychological Reactance to Chatbot Feedback Stylescited by 1
- 2026A Conditional Companion: Lived Experiences of People with Mental Health Disorders Using LLMscited by 1
- 2026Human-Centred LLM Privacy Audits: Findings and Frictionscited by 1
- 2026DysLexLens: A Low-Resource LLM Framework for Analysing Dyslexic Learners Insights from Online Forumscited by 0
- 2026Right Words, Wrong Moment: A Clinician-Grounded Analysis of Distress in 19,930 Conversations between Young People and ChatGPTcited by 0
- 2026Right Words, Wrong Moment: A Clinician-Grounded Analysis of Distress in 19,930 Conversations between Young People and ChatGPT28 Sep 2026
- 2026DysLexLens: A Low-Resource LLM Framework for Analysing Dyslexic Learners Insights from Online Forums26 Jun 2026
- 2026Human-Centred LLM Privacy Audits: Findings and Frictions12 Mar 2026
- 2026A Conditional Companion: Lived Experiences of People with Mental Health Disorders Using LLMs30 Jan 2026
- 2026AI and My Values: User Perceptions of LLMs' Ability to Extract, Embody, and Explain Human Values from Casual Conversations30 Jan 2026
- 2026Polite But Boring? Trade-offs Between Engagement and Psychological Reactance to Chatbot Feedback Styles28 Jan 2026
- 2025Mental Models of Autonomy and Sentience Shape Reactions to AI9 Dec 2025
- 2025Empathy-R1: A Chain-of-Empathy and Reinforcement Learning Framework for Long-Form Mental Health Support18 Sep 2025