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Activating Social Responses in Human–AI Interaction: The Effects of Reciprocal Self-Disclosure and Social Role Framing
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- TRV-2026-2659raw data (JSON)
- Version
- v1Published
- First published
- Evidence type
- Controlled or validated comparison
- Sources
- 1 sourcePeer-reviewed
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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 d…
The problem
In an experiment with 169 participants, a chatbot that reciprocated self-disclosure did not significantly raise users' trust or perceived support, and users reported lower enjoyment than with a non-reciprocating chatbot.
Sources
1 on file- International Journal of Human–Computer Interactiondoi:10.1080/10447318.2026.2738478
Cite this record
v1Truvace Impact Record TRV-2026-2659, v1: “Activating Social Responses in Human–AI Interaction: The Effects of Reciprocal Self-Disclosure and Social Role Framing.” Truvace, 2026-11-12. /record/TRV-2026-2659 (accessed on the day you copy it). sha256 b6ec785553a4cf4c…
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