Like a Therapist, But Not: Reddit Narratives of AI in Mental Health Contexts
Elham Aghakhani, Rezvaneh Rezapour
TL;DR
This study addresses how people perceive and engage with AI tools for mental health in real-world, non-clinical settings, using 5,126 experiential or exploratory Reddit posts from 47 communities (Nov 2022–Aug 2025). It develops a theory-informed annotation framework by integrating the Technology Acceptance Model and therapeutic alliance to quantify evaluative language, adoption attitudes, and relational alignment with AI. Key findings show engagement is driven by narrated outcomes, trust, and response quality rather than emotional bonding alone; task and goal alignment predict positive sentiment, while companionship-leaning uses raise risks such as dependence and symptom escalation. The work contributes a novel discourse dataset, a theory-grounded NLP annotation approach, and empirical insights with implications for designing, evaluating, and governing AI-enabled mental health tools in real-world contexts.
Abstract
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.