LLM Use for Mental Health: Crowdsourcing Users' Sentiment-based Perspectives and Values from Social Discussions

• URL: http://arxiv.org/abs/2512.07797v1
• Date: Mon, 08 Dec 2025 18:29:06 GMT
• Title: LLM Use for Mental Health: Crowdsourcing Users' Sentiment-based Perspectives and Values from Social Discussions
• Authors: Lingyao Li, Xiaoshan Huang, Renkai Ma, Ben Zefeng Zhang, Haolun Wu, Fan Yang, Chen Chen,
• Abstract summary: Large language models (LLMs) are increasingly used for mental health support.<n>They raise concerns about misinformation, over-reliance, and risks in high-stakes contexts of mental health.<n>We crowdsource large-scale users' posts from six major social media platforms.
• Score: 14.924411621735471
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) chatbots like ChatGPT are increasingly used for mental health support. They offer accessible, therapeutic support but also raise concerns about misinformation, over-reliance, and risks in high-stakes contexts of mental health. We crowdsource large-scale users' posts from six major social media platforms to examine how people discuss their interactions with LLM chatbots across different mental health conditions. Through an LLM-assisted pipeline grounded in Value-Sensitive Design (VSD), we mapped the relationships across user-reported sentiments, mental health conditions, perspectives, and values. Our results reveal that the use of LLM chatbots is condition-specific. Users with neurodivergent conditions (e.g., ADHD, ASD) report strong positive sentiments and instrumental or appraisal support, whereas higher-risk disorders (e.g., schizophrenia, bipolar disorder) show more negative sentiments. We further uncover how user perspectives co-occur with underlying values, such as identity, autonomy, and privacy. Finally, we discuss shifting from "one-size-fits-all" chatbot design toward condition-specific, value-sensitive LLM design.

Related papers

• Exploring User Security and Privacy Attitudes and Concerns Toward the Use of General-Purpose LLM Chatbots for Mental Health [5.3052849646510225]
  Large language model (LLM)-enabled conversational agents for emotional support are increasingly being used by individuals.<n>Little empirical research measures users' privacy and security concerns, attitudes, and expectations.<n>We identify critical misconceptions and a general lack of risk awareness.<n>We propose recommendations to safeguard user mental health disclosures.
  arXiv  Detail & Related papers  (2025-07-14T18:10:21Z)
• SocialEval: Evaluating Social Intelligence of Large Language Models [70.90981021629021]
  Social Intelligence (SI) equips humans with interpersonal abilities to behave wisely in navigating social interactions to achieve social goals.<n>This presents an operational evaluation paradigm: outcome-oriented goal achievement evaluation and process-oriented interpersonal ability evaluation.<n>We propose SocialEval, a script-based bilingual SI benchmark, integrating outcome- and process-oriented evaluation by manually crafting narrative scripts.
  arXiv  Detail & Related papers  (2025-06-01T08:36:51Z)
• Sentient Agent as a Judge: Evaluating Higher-Order Social Cognition in Large Language Models [75.85319609088354]
  Sentient Agent as a Judge (SAGE) is an evaluation framework for large language models.<n>SAGE instantiates a Sentient Agent that simulates human-like emotional changes and inner thoughts during interaction.<n>SAGE provides a principled, scalable and interpretable tool for tracking progress toward genuinely empathetic and socially adept language agents.
  arXiv  Detail & Related papers  (2025-05-01T19:06:10Z)
• AI Chatbots for Mental Health: Values and Harms from Lived Experiences of Depression [5.093381538166489]
  This work explores the relationship between lived experience values, potential harms, and design recommendations for mental health AI chatbots.<n>We developed a technology probe, a GPT-4o based chatbots called Zenny, enabling participants to engage with depression self-management scenarios.<n>Our thematic analysis revealed key values: informational support, emotional support, personalization, privacy, and crisis management.
  arXiv  Detail & Related papers  (2025-04-26T14:17:25Z)
• "It Listens Better Than My Therapist": Exploring Social Media Discourse on LLMs as Mental Health Tool [1.223779595809275]
  Large language models (LLMs) offer new capabilities in conversational fluency, empathy simulation, and availability.<n>This study explores how users engage with LLMs as mental health tools by analyzing over 10,000 TikTok comments.<n>Results show that nearly 20% of comments reflect personal use, with these users expressing overwhelmingly positive attitudes.
  arXiv  Detail & Related papers  (2025-04-14T17:37:32Z)
• LLM Questionnaire Completion for Automatic Psychiatric Assessment [49.1574468325115]
  We employ a Large Language Model (LLM) to convert unstructured psychological interviews into structured questionnaires spanning various psychiatric and personality domains. The obtained answers are coded as features, which are used to predict standardized psychiatric measures of depression (PHQ-8) and PTSD (PCL-C)
  arXiv  Detail & Related papers  (2024-06-09T09:03:11Z)
• Can AI Relate: Testing Large Language Model Response for Mental Health Support [23.97212082563385]
  Large language models (LLMs) are already being piloted for clinical use in hospital systems like NYU Langone, Dana-Farber and the NHS. We develop an evaluation framework for determining whether LLM response is a viable and ethical path forward for the automation of mental health treatment.
  arXiv  Detail & Related papers  (2024-05-20T13:42:27Z)
• The Typing Cure: Experiences with Large Language Model Chatbots for Mental Health Support [32.60242402941811]
  People experiencing severe distress increasingly use Large Language Model (LLM) chatbots as mental health support tools.<n>This study builds on interviews with 21 individuals from globally diverse backgrounds to analyze how users create unique support roles.<n>We introduce the concept of therapeutic alignment, or aligning AI with therapeutic values for mental health contexts.
  arXiv  Detail & Related papers  (2024-01-25T18:08:53Z)
• Who is ChatGPT? Benchmarking LLMs' Psychological Portrayal Using PsychoBench [83.41621219298489]
  We propose a framework, PsychoBench, for evaluating diverse psychological aspects of Large Language Models (LLMs) PsychoBench classifies these scales into four distinct categories: personality traits, interpersonal relationships, motivational tests, and emotional abilities. We employ a jailbreak approach to bypass the safety alignment protocols and test the intrinsic natures of LLMs.
  arXiv  Detail & Related papers  (2023-10-02T17:46:09Z)
• Emotionally Numb or Empathetic? Evaluating How LLMs Feel Using EmotionBench [83.41621219298489]
  We evaluate Large Language Models' (LLMs) anthropomorphic capabilities using the emotion appraisal theory from psychology. We collect a dataset containing over 400 situations that have proven effective in eliciting the eight emotions central to our study. We conduct a human evaluation involving more than 1,200 subjects worldwide.
  arXiv  Detail & Related papers  (2023-08-07T15:18:30Z)
• Mental Illness Classification on Social Media Texts using Deep Learning and Transfer Learning [55.653944436488786]
  According to the World health organization (WHO), approximately 450 million people are affected. Mental illnesses, such as depression, anxiety, bipolar disorder, ADHD, and PTSD. This study analyzes unstructured user data on Reddit platform and classifies five common mental illnesses: depression, anxiety, bipolar disorder, ADHD, and PTSD.
  arXiv  Detail & Related papers  (2022-07-03T11:33:52Z)
• Assessing the Severity of Health States based on Social Media Posts [62.52087340582502]
  We propose a multiview learning framework that models both the textual content as well as contextual-information to assess the severity of the user's health state. The diverse NLU views demonstrate its effectiveness on both the tasks and as well as on the individual disease to assess a user's health.
  arXiv  Detail & Related papers  (2020-09-21T03:45:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.