Previously submitted to: JMIR Mental Health (no longer under consideration since Nov 15, 2025)
Date Submitted: Nov 12, 2025
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
User Perceptions of Artificial Intelligence in Mental Healthcare: A Mixed-Methods Discourse Analysis of YouTube Commentary
ABSTRACT
Background:
A critical knowledge gap exists between controlled research findings and naturalistic user experiences that determine real-world adoption of AI mental health applications. Public discourse surrounding these tools reflects complex negotiations between technological potential and therapeutic relationship requirements inadequately characterised in peer-reviewed literature.
Objective:
This investigation examined public perceptions of AI mental health applications through systematic analysis of YouTube user discourse, identifying patterns of acceptance, resistance, and technological appropriation.
Methods:
We employed a convergent mixed-methods design, analysing 8,349 comments from ten strategically sampled YouTube videos (January 2023–August 2025). Quantitative analysis utilised VADER sentiment classification, NRC emotion lexicon analysis, and Latent Dirichlet Allocation topic modelling. Qualitative analysis employed reflexive thematic analysis with triangulation through comparison matrices.
Results:
Sentiment analysis revealed 56.5% positive comments overall, though negative sentiment increased to 32.4% when weighted by user engagement, indicating critical perspectives achieved disproportionate audience resonance. Five major themes emerged: AI as a superior alternative emphasising consistency; practical advantages including accessibility; irreplaceable value of human connection; risks encompassing technical limitations; and AI as a complementary tool with optimisation strategies. Topic modelling corroborated findings with "AI-assisted venting," "therapy accessibility," and "human-AI comparison" as primary discourse clusters.
Conclusions:
Public discourse reflects pragmatic positioning toward AI mental health tools, characterised by conditional acceptance dependent upon integration with human oversight rather than wholesale replacement. Users demonstrate sophisticated understanding of capabilities and limitations, advocating for hybrid care models that preserve human expertise while leveraging AI's accessibility advantages.
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Copyright
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