"I Hear You, I Feel You": Encouraging Deep Self-disclosure through a Chatbot


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.

Self-disclosure, Mental well-being
Yi-Chieh Lee
University of Illinois at Urbana-Champaign & NTT Japan, Champaign, IL, USA
Naomi Yamashita
NTT Japan, Keihanna, Japan
Yun Huang
University of Illinois at Urbana-Champaign, Champaign, IL, USA
Wai Fu
University of Illinois at Urbana-Champaign, Champaign, IL, USA




会議: CHI 2020

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2020.acm.org/)

セッション: Chatbots

Paper session
5 件の発表
2020-04-30 20:00:00
2020-04-30 21:15:00