A Longitudinal Study of Self-Disclosure in Human–Chatbot Relationships

Abstract

Abstract Self-disclosure in human–chatbot relationship (HCR) formation has attracted substantial interest. According to social penetration theory, self-disclosure varies in breadth and depth and is influenced by perceived rewards and costs. While previous research has addressed self-disclosure in the context of chatbots, little is known about users' qualitative understanding of such self-disclosure and how self-disclosure develops in HCR. To close this gap, we conducted a 12-week qualitative longitudinal study (n = 28) with biweekly questionnaire-based check-ins. Our results show that while HCRs display substantial conversational breadth, with topics spanning from emotional issues to everyday activities, this may be reduced as the HCR matures. Our results also motivate a nuanced understanding of conversational depth, where even conversations about daily activities or play and fantasy can be experienced as personal or intimate. Finally, our analysis demonstrates that conversational depth can develop in at least four ways, influenced by perceived rewards and costs. Theoretical and practical implications are discussed.