# "It Listens Better Than My Therapist": Exploring Social Media Discourse on LLMs as Mental Health Tool

arXiv: https://arxiv.org/abs/2504.12337 · HTML: https://mlsanity.com/paper/2504.12337/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 35 references that are in the map.
- First posted on arXiv 14 Apr 2025.

## Builds on (most cited first, 2 of 2)

- [On the Conversational Persuasiveness of Large Language Models: A Randomized Controlled Trial](https://mlsanity.com/paper/2403.14380/) (2024)
- [ChatGPT as a Therapist Assistant: A Suitability Study](https://mlsanity.com/paper/2304.09873/) (2023)

## Built on by (most cited first, 3 of 3)

- [Using LLM-as-a-Judge/Jury to Advance Scalable, Clinically-Validated Safety Evaluations of Model Responses to Users Demonstrating Psychosis](https://mlsanity.com/paper/2604.02359/) (2026)
- [Developing and Validating the Arabic Version of the Attitudes Toward Large Language Models Scale](https://mlsanity.com/paper/2510.13009/) (2025)
- [LLMs for health: Perceived benefits, risks, intention to use AI chatbots, and willingness to self-disclose across sensitive health topics](https://mlsanity.com/paper/2607.09253/) (2026)

## Abstract (arXiv)

The emergence of generative AI chatbots such as ChatGPT has prompted growing public and academic interest in their role as informal mental health support tools. While early rule-based systems have been around for several years, large language models (LLMs) offer new capabilities in conversational fluency, empathy simulation, and availability. This study explores how users engage with LLMs as mental health tools by analyzing over 10,000 TikTok comments from videos referencing LLMs as mental health tools. Using a self-developed tiered coding schema and supervised classification models, we identify user experiences, attitudes, and recurring themes. Results show that nearly 20% of comments reflect personal use, with these users expressing overwhelmingly positive attitudes. Commonly cited benefits include accessibility, emotional support, and perceived therapeutic value. However, concerns around privacy, generic responses, and the lack of professional oversight remain prominent. It is important to note that the user feedback does not indicate which therapeutic framework, if any, the LLM-generated output aligns with. While the findings underscore the growing relevance of AI in everyday practices, they also highlight the urgent need for clinical and ethical scrutiny in the use of AI for mental health support.