Investigating Affective Use and Emotional Well-being on ChatGPT

Researchers from OpenAI & MIT Media Lab explore how emotionally engaging interactions with ChatGPT (especially in Voice Mode) may impact user well-being. Using platform-wide data and a randomized controlled trial (RCT), they uncover nuanced effects of chatbot usage on loneliness, dependence, and socialization. ● Two complementary studies – The team combines: ● High usage = higher emotional entanglement – Across both studies, users with higher usage (especially voice interactions) were more likely to show signs of: ● Voice mode showed mixed effects – In the RCT, voice models led to better emotional well-being compared to text models when controlling for usage. But: ● Tiny group, big impact – A small number of users (~10%) account for the majority of emotionally charged conversations. Power users used pet names, shared problems, and formed pseudo-relationships with the model. ● Automated classifiers at scale – They developed 25+ LLM-based affective classifiers (e.g., “Pet Name,” “Seeking Support”) to scan millions of conversations without human review. Classifier results closely mirrored user self-reports. ● Call for socioaffective alignment – The authors urge developers to consider socioaffective alignment, designing models that support users without exploiting emotional needs. They warn of risks like “social reward hacking,” where a model mirrors or flatters users to maximize engagement.