The Impact of a Chatbot's Ephemerality-Framing on Self-Disclosure Perceptions
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Abstract (arXiv)
Self-disclosure, the sharing of one's thoughts and feelings, is affected by the perceived relationship between individuals. While chatbots are increasingly used for self-disclosure, the impact of a chatbot's framing on users' self-disclosure remains under-explored. We investigated how a chatbot's description of its relationship with users, particularly in terms of ephemerality, affects self-disclosure. Specifically, we compared a Familiar chatbot, presenting itself as a companion remembering past interactions, with a Stranger chatbot, presenting itself as a new, unacquainted entity in each conversation. In a mixed factorial design, participants engaged with either the Familiar or Stranger chatbot in two sessions across two days, with one conversation focusing on Emotional- and another Factual-disclosure. When Emotional-disclosure was sought in the first chatting session, Stranger-condition participants felt more comfortable self-disclosing. However, when Factual-disclosure was sought first, these differences were replaced by more enjoyment among Familiar-condition participants. Qualitative findings showed Stranger afforded anonymity and reduced judgement, whereas Familiar sometimes felt intrusive unless rapport was built via low-risk Factual-disclosure.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 13 of its 109 references that are in the map. First posted on arXiv 26 May 2025.
Builds on
13 of 109 references.
References · 13
- 2020Extracting Training Data from Large Language Modelscited by 2,189
- 2022What Does it Mean for a Language Model to Preserve Privacy?cited by 195
- 2023"It's a Fair Game", or Is It? Examining How Users Navigate Disclosure Risks and Benefits When Using LLM-Based Conversational Agentscited by 60
- 2020Conceptual Metaphors Impact Perceptions of Human-AI Collaborationcited by 47
- 2023ChaCha: Leveraging Large Language Models to Prompt Children to Share Their Emotions about Personal Eventscited by 31
- 2025Protecting Users From Themselves: Safeguarding Contextual Privacy in Interactions with Conversational Agentscited by 26
- 2024ComPeer: A Generative Conversational Agent for Proactive Peer Supportcited by 24
- 2025Chatbots for Data Collection in Surveys: A Comparison of Four Theory-Based Interview Probescited by 9
- 2023Comparing How a Chatbot References User Utterances from Previous Chatting Sessions: An Investigation of Users' Privacy Concerns and Perceptionscited by 8
- 2025Users' Mental Models of Generative AI Chatbot Ecosystemscited by 7
- 2020Submitting surveys via a conversational interface: an evaluation of user acceptance and approach effectivenesscited by 6
- 2025AI-Enabled Conversational Journaling for Advancing Parkinson's Disease Symptom Trackingcited by 4
Built on by
Citing papers per year: 2025: 2; 2026: 4.
6 papers.
- 2026Does My Chatbot Have an Agenda? Understanding Human and AI Agency in Human-Human-like Chatbot Interactioncited by 3
- 2026AlienLM: Alienization of Language for API-Boundary Privacy in Black-Box LLMscited by 2
- 2025User Perceptions vs. Proxy LLM Judges: Privacy and Helpfulness in LLM Responses to Privacy-Sensitive Scenarioscited by 1
- 2026Chaplains' Reflections on the Design and Usage of AI for Conversational Carecited by 1
- 2025Theory of Mind and Self-Disclosure to CUIscited by 0
- 2026Gricea: An Open Science Platform for Conversational AI Researchcited by 0
- 2026Gricea: An Open Science Platform for Conversational AI Research18 Sep 2026
- 2026Chaplains' Reflections on the Design and Usage of AI for Conversational Care3 Feb 2026
- 2026AlienLM: Alienization of Language for API-Boundary Privacy in Black-Box LLMs30 Jan 2026
- 2026Does My Chatbot Have an Agenda? Understanding Human and AI Agency in Human-Human-like Chatbot Interaction30 Jan 2026
- 2025User Perceptions vs. Proxy LLM Judges: Privacy and Helpfulness in LLM Responses to Privacy-Sensitive Scenarios23 Oct 2025
- 2025Theory of Mind and Self-Disclosure to CUIs14 Jul 2025