Activating Social Responses in Human–AI Interaction: The Effects of Reciprocal Self-Disclosure and Social Role Framing

As chatbots become increasingly embedded in everyday communication, understanding when they elicit social responses is important. We conducted an experiment in which 169 participants interacted with LLM-based chatbots in a 2 (reciprocal self-disclosure: yes vs. no) × 3 (social role: mentor, companion, or assistant) between-subjects design. Self-esteem was measured before the interaction as an individual-difference factor. Factorial analyses showed that reciprocal self-disclosure increased users’ disclosure but did not significantly increase trust or perceived support; enjoyment was lower in the reciprocal-disclosure condition. Social role interacted with self-esteem in predicting self-disclosure quality, with favorable responses to the mentor role among participants with higher self-esteem. These findings suggest that chatbot social cues elicit different behavioral and evaluative responses and that role framing should account for user characteristics. The study offers a conditional account of social responding in human–AI interaction and implications for chatbot design in relational contexts.