Exploring the Effects of Self-Mockery to Improve Task-Oriented Chatbot’s Social Intelligence
Published in DIS' 22, 2022
This study explores the impact of self-mockery humor on a customer service chatbot's Social Intelligence (SI) by proposing a pipeline for incorporating situated self-mockery in different interaction stages. Through a within-subject experiment involving 28 participants, the self-mockery chatbot was found to be significantly funnier, more satisfactory, and to perform better in certain aspects of SI compared to a chatbot without self-mockery utterances. The study also discusses individual factors influencing the perceived helpfulness of self-mockery on SI and provides design considerations based on the findings.
Recommended citation: Chengzhong Liu, Shixu Zhou, Yuanhao Zhang, Dingdong Liu, Zhenhui Peng, and Xiaojuan Ma. 2022. Exploring the Effects of Self-Mockery to Improve Task-Oriented Chatbot’s Social Intelligence. In Proceedings of the 2022 ACM Designing Interactive Systems Conference (DIS ‘22). Association for Computing Machinery, New York, NY, USA, 1315-1329. https://doi.org/10.1145/3532106.3533461
Recommended citation: Chengzhong Liu, Shixu Zhou, Yuanhao Zhang, Dingdong Liu, Zhenhui Peng, and Xiaojuan Ma. 2022. Exploring the Effects of Self-Mockery to Improve Task-Oriented Chatbot's Social Intelligence. In Proceedings of the 2022 ACM Designing Interactive Systems Conference (DIS '22). Association for Computing Machinery, New York, NY, USA, 1315-1329. https://doi.org/10.1145/3532106.3533461
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