# Like a Therapist, But Not: Reddit Narratives of AI in Mental Health Contexts

arXiv: https://arxiv.org/abs/2601.20747 · HTML: https://mlsanity.com/paper/2601.20747/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 74 references that are in the map.
- First posted on arXiv 28 Jan 2026.

## Builds on (most cited first, 9 of 9)

- [Language (Technology) is Power: A Critical Survey of "Bias" in NLP](https://mlsanity.com/paper/2005.14050/) (2020)
- [A Computational Approach to Understanding Empathy Expressed in Text-Based Mental Health Support](https://mlsanity.com/paper/2009.08441/) (2020)
- [LLM-in-the-loop: Leveraging Large Language Model for Thematic Analysis](https://mlsanity.com/paper/2310.15100/) (2023)
- [The Role of AI in Peer Support for Young People: A Study of Preferences for Human- and AI-Generated Responses](https://mlsanity.com/paper/2405.02711/) (2024)
- [Decoding the Narratives: Analyzing Personal Drug Experiences Shared on Reddit](https://mlsanity.com/paper/2406.12117/) (2024)
- [COMPASS: Computational Mapping of Patient-Therapist Alliance Strategies with Language Modeling](https://mlsanity.com/paper/2402.14701/) (2024)
- [Talk, Listen, Connect: How Humans and AI Evaluate Empathy in Responses to Emotionally Charged Narratives](https://mlsanity.com/paper/2409.15550/) (2024)
- [From Conversation to Automation: Leveraging LLMs for Problem-Solving Therapy Analysis](https://mlsanity.com/paper/2501.06101/) (2025)
- [Research Community Perspectives on "Intelligence" and Large Language Models](https://mlsanity.com/paper/2505.20959/) (2025)

## Built on by (most cited first, 2 of 2)

- [Self-Stigma Is Not a Monolith, but Generic Empathy Is: Persona-Conditioned LLM Support for People Who Use Drugs](https://mlsanity.com/paper/2606.23387/) (2026)
- [Beyond Binary Detection: A Multi-Dimensional Taxonomy of Cancer Misinformation on Reddit](https://mlsanity.com/paper/2607.12383/) (2026)

## Abstract (arXiv)

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.