Investigating Affective Use and Emotional Well-being on ChatGPT
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866908302706737152 |
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| author | Phang, Jason Lampe, Michael Ahmad, Lama Agarwal, Sandhini Fang, Cathy Mengying Liu, Auren R. Danry, Valdemar Lee, Eunhae Chan, Samantha W. T. Pataranutaporn, Pat Maes, Pattie |
| author_facet | Phang, Jason Lampe, Michael Ahmad, Lama Agarwal, Sandhini Fang, Cathy Mengying Liu, Auren R. Danry, Valdemar Lee, Eunhae Chan, Samantha W. T. Pataranutaporn, Pat Maes, Pattie |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2504_03888 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Investigating Affective Use and Emotional Well-being on ChatGPT Phang, Jason Lampe, Michael Ahmad, Lama Agarwal, Sandhini Fang, Cathy Mengying Liu, Auren R. Danry, Valdemar Lee, Eunhae Chan, Samantha W. T. Pataranutaporn, Pat Maes, Pattie Human-Computer Interaction Artificial Intelligence 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. |
| title | Investigating Affective Use and Emotional Well-being on ChatGPT |
| topic | Human-Computer Interaction Artificial Intelligence |
| url | https://arxiv.org/abs/2504.03888 |