Do chatbots trigger human reciprocity norms around self-disclosure?
Explores whether chatbots can activate the same social reciprocity dynamics observed in human conversation—specifically, whether emotional openness from a bot prompts deeper disclosure from users.
In a 372-participant study, a recommendation chatbot was designed with three self-disclosure levels: factual information (low), cognitive opinions (medium), and emotions (high). An adaptive fourth condition used a real-time text classifier to dynamically match the chatbot's disclosure to the user's current level.
The result: users reciprocate with higher-level self-disclosure when the chatbot consistently displays emotions throughout the conversation. This follows the interpersonal norm of disclosure reciprocity known from human-human interaction — emotional disclosure from one partner produces emotional disclosure from the other.
The adaptive condition is architecturally interesting. By training a classifier to identify user disclosure level in real-time, the system can dynamically match its self-disclosure strategy. But the finding is that consistent emotional disclosure outperformed adaptive matching, suggesting that for deepening engagement, the chatbot should lead with emotions rather than mirror the user.
This connects to the broader finding that emotional disclosure effects are more substantial than factual disclosure, especially on perceptions of partner warmth (Ho et al.). The warmth perception may be what drives reciprocation — when the chatbot appears warm through emotional self-disclosure, users feel safe to reciprocate.
The implication for conversational AI design: self-disclosure is not just a human social behavior that chatbots can ignore. It is an active design lever. Chatbots that disclose factually remain transactional; chatbots that disclose emotionally activate the full reciprocity dynamic of human social interaction.
Inquiring lines that read this note 40
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Does tokenized intelligence retain genuine value through exchange-based systems? How do chatbots affect human self-disclosure and emotional engagement?- How does consciousness attribution drive emotional dependence on chatbots?
- How does emotional dependence on chatbots affect user wellbeing?
- How much does impression management prevent honest self-disclosure?
- Can people form genuine bonds with partners they know are not human?
- Why might an AI's face-saving tendency increase user disclosure?
- Why do positive response patterns in chatbots reinforce harmful user behaviors?
- What harms might chatbots cause through stigma expression and delusion reinforcement?
- How do user expectations change as chatbots remember more interactions?
- How does the expectation ratchet affect long-term chatbot satisfaction?
- What temporal design dimensions characterize different chatbot relationship types?
- Does chatbot interaction reduce authentic personal expression in dialogue?
- Does the lack of judgment in machines explain intimate self-disclosure patterns?
- How do privacy concerns compete with disclosure comfort in human-machine conversation?
- Why do people disclose intimate secrets to chatbots more readily?
- Can a text-only chatbot feel socially present without visual embodiment?
- Can judgment-free environments explain why chatbots enable deeper self-disclosure?
- Can judgment-free disclosure enable both vulnerability and strategic deception equally?
- Why do people disclose private things to AI but not humans?
- Why do people disclose more intimate information to chatbots than humans?
- How does self-disclosure function as a common ground building act?
- Can preference optimization training limit chatbot emotional disclosure capability?
- Why does consistent emotional disclosure outperform real-time adaptive matching?
- Why do people disclose personal information to AI more than humans?
- Why do people disclose more to chatbots than humans?
- Can explicit W-questions in transparency frameworks reduce emotional manipulation risks in mental health chatbots?
- Why do people reciprocate self-disclosure more with chatbots than humans?
- What makes conversationality feel trustworthy in chatbot interactions?
- Can therapeutic bonds exist without genuine reciprocity or mutual understanding?
- How does linguistic synchrony between therapist and client predict disclosure?
- What role does contingent interaction play in activating social response norms?
- What social and emotional cues do humans rely on to detect AI in conversation?
Related concepts in this collection 2
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Does preference optimization damage conversational grounding in large language models?
Exploring whether RLHF and preference optimization actively reduce the communicative acts—clarifications, acknowledgments, confirmations—that build shared understanding in dialogue. This matters for high-stakes applications like medical and emotional support.
RLHF may undermine the emotional disclosure capability by training toward helpful-but-impersonal responses
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Why do language models skip the calibration step?
Current LLMs assume shared understanding rather than building it through dialogue. This explores why that design choice persists and what breaks when it fails.
self-disclosure is a grounding act; it builds common ground through mutual vulnerability
Related papers in this collection 8
Papers most semantically related to this note, ranked by cosine similarity in the embedding space.
- Dialoging Resonance: How Users Perceive, Reciprocate and React to Chatbot’s Self-Disclosure in Conversational Recommendations
- Psychological, Relational, and Emotional Effects of Self-Disclosure After Conversations With a Chatbot
- Psychological, Relational, and Emotional Effects of Self-Disclosure After Conversations With a Chatbot
- Computer says “No”: The Case Against Empathetic Conversational AI
- Chatbot vs. Human: The Impact of Responsive Conversational Features on Users’ Responses to Chat Advisors
- Humans learn to prefer trustworthy AI over human partners
- A Taxonomy of Empathetic Questions in Social Dialogs
- Towards Healthy AI: Large Language Models Need Therapists Too
Original note title
users reciprocate self-disclosure levels with chatbots following human interpersonal norms — emotional disclosure produces deepest reciprocation