The Day My Chatbot Changed: Characterizing the Mental Health Impacts of Social AI App Updates via Negative User Reviews

TL;DR

This study analyzes over 210,000 Google Play reviews of a social AI chatbot to understand how app updates influence user ratings, concerns, and potential mental health impacts.

Contribution

It links user reviews to specific app versions, revealing how updates affect user satisfaction and highlight recurring issues and psychological concerns.

Findings

User ratings fluctuate with app versions, with some updates linked to more negative reviews.

Technical malfunctions and errors are common themes in dissatisfied reviews.

Some reviews express concerns about psychological or addiction-related effects.

Abstract

Artificial Intelligence (AI) chatbots are increasingly used for emotional, creative, and social support, leading to sustained and routine user interaction with these systems. As these applications evolve through frequent version updates, changes in functionality or behavior may influence how users evaluate them. However, work on how publicly expressed user feedback varies across app versions in real-world deployment contexts is limited. This study analyzes 210,840 Google Play reviews of the chatbot application Character AI, linking each review to the app version active at the time of posting. We specifically examine negative reviews to study how version-level rating trends, and linguistic patterns reflect user experiences. Our results show that user ratings fluctuate across successive versions, with certain releases associated with stronger negative evaluations. Thematic analysis indicates that dissatisfaction is concentrated around recurring issues related to technical malfunctions and errors. A subset of reviews additionally frames these concerns in terms of potential psychological or addiction-related effects. The findings highlight how aggregate user evaluations and expressed concerns vary across software iterations and provide empirical insight into how update cycles relate to user feedback patterns and underscore the importance of stability and transparent communication in evolving AI systems.