"I Hear You, I Feel You": Encouraging Deep Self-disclosure through a Chatbot
By Yi-Chieh Lee, Naomi Yamashita, Yun Huang, Wai-Tat Fu. International Conference on Human Factors in Computing Systems, 2020
348 citations recorded by the source corpus.
Chatbots have great potential to serve as a low-cost, effective tool to support people's self-disclosure. Prior work has shown that reciprocity occurs in human-machine dialog; however, whether reciprocity can be leveraged to promote and sustain deep self-disclosure over time has not been systematically studied. In this work, we design, implement and evaluate a chatbot that has self-disclosure features when it performs small talk with people. We ran a study with 47 participants and divided them into three groups to use different chatting styles of the chatbot for three weeks. We found that chatbot self-disclosure had a reciprocal effect on promoting deeper participant self-disclosure that lasted over the study period, in which the other chat styles without self-disclosure features failed to deliver. Chatbot self-disclosure also had a positive effect on improving participants' perceived intimacy and enjoyment over the study period. Finally, we reflect on the design implications of chatbots where deep self-disclosure is needed over time.
Subfields: Applied Psychology, Social Psychology, Sociology and Political Science
View Paper: https://www.semanticscholar.org/paper/dd99f5cb66b6243c24929ad6a5a6edde5d821b1e
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In Social Psychology
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Citations in the corpus
- My Chatbot Companion - a Study of Human-Chatbot Relationships (2021, background): “Previous research has shown that a chatbot ’ s level of reciprocity can influence users ’ willingness to self-disclose as well as the perceived level of intimacy and enjoyment (Lee et al., 2020).”