# Investigating Affective Use and Emotional Well-being on ChatGPT

arXiv: https://arxiv.org/abs/2504.03888 · HTML: https://mlsanity.com/paper/2504.03888/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Apr 2025.

## Built on by (most cited first, 30 of 38)

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- [Detecting and Preventing Harmful Behaviors in AI Companions: Development and Evaluation of the SHIELD Supervisory System](https://mlsanity.com/paper/2510.15891/) (2025)
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- [MindGuard: Guardrail Classifiers for Multi-Turn Mental Health Support](https://mlsanity.com/paper/2602.00950/) (2026)
- [Disclosure By Design: Identity Transparency as a Behavioural Property of Conversational AI Models](https://mlsanity.com/paper/2603.16874/) (2026)
- [What Does 'Human-Centred AI' Mean?](https://mlsanity.com/paper/2507.19960/) (2025)
- [Understanding Usage and Engagement in AI-Powered Scientific Research Tools: The Asta Interaction Dataset](https://mlsanity.com/paper/2602.23335/) (2026)
- [CharacterFlywheel: Scaling Iterative Improvement of Engaging and Steerable LLMs in Production](https://mlsanity.com/paper/2603.01973/) (2026)
- [Discourse Diversity in Multi-Turn Empathic Dialogue](https://mlsanity.com/paper/2604.11742/) (2026)
- [HEART-Bench: Do LLM Agents Exhibit Human-like Psychology?](https://mlsanity.com/paper/2605.30058/) (2026)
- [Caring Without Feeling: Affective Dynamics as the Control Layer of Human-AI Agent Collaboration](https://mlsanity.com/paper/2606.18259/) (2026)
- [TRACE: Temporal Relationship-Aware Conversational Entrainment Detection in Dyadic Speech](https://mlsanity.com/paper/2606.30543/) (2026)
- [Emotionally Vulnerable Subtype of Internet Gaming Disorder: Measuring and Exploring the Pathology of Problematic Generative AI Use](https://mlsanity.com/paper/2510.06908/) (2025)
- [Generative Confidants: How do People Experience Trust in Emotional Support from Generative AI?](https://mlsanity.com/paper/2601.16656/) (2026)
- [Visual Affect Analysis: Predicting Emotions of Image Viewers with Vision-Language Models](https://mlsanity.com/paper/2602.00123/) (2026)
- [Mapping generative AI use in the human brain: divergent neural, academic, and mental health profiles of functional versus socio emotional AI use](https://mlsanity.com/paper/2604.08594/) (2026)
- [Spontaneous Persuasion: An Audit of Model Persuasiveness in Everyday Conversations](https://mlsanity.com/paper/2604.22109/) (2026)
- [How Value Induction Reshapes LLM Behaviour](https://mlsanity.com/paper/2605.07925/) (2026)
- [Playing Games with My Heart: An Evaluation of AI Companion Apps](https://mlsanity.com/paper/2605.08093/) (2026)
- [How Early Adopters Used Generative AI Worldwide: Variation by Country Income and Language](https://mlsanity.com/paper/2605.30685/) (2026)
- [VET: A Framework for Analyzing AI Discourse](https://mlsanity.com/paper/2606.01929/) (2026)

## Abstract (arXiv)

As AI chatbots see increased adoption and integration into everyday life, questions have been raised about the potential impact of human-like or anthropomorphic AI on users. In this work, we investigate the extent to which interactions with ChatGPT (with a focus on Advanced Voice Mode) may impact users' emotional well-being, behaviors and experiences through two parallel studies. To study the affective use of AI chatbots, we perform large-scale automated analysis of ChatGPT platform usage in a privacy-preserving manner, analyzing over 3 million conversations for affective cues and surveying over 4,000 users on their perceptions of ChatGPT. To investigate whether there is a relationship between model usage and emotional well-being, we conduct an Institutional Review Board (IRB)-approved randomized controlled trial (RCT) on close to 1,000 participants over 28 days, examining changes in their emotional well-being as they interact with ChatGPT under different experimental settings. In both on-platform data analysis and the RCT, we observe that very high usage correlates with increased self-reported indicators of dependence. From our RCT, we find that the impact of voice-based interactions on emotional well-being to be highly nuanced, and influenced by factors such as the user's initial emotional state and total usage duration. Overall, our analysis reveals that a small number of users are responsible for a disproportionate share of the most affective cues.