Desirable or Distasteful? Exploring Uncertainty in Human-Chatbot Relationships

聊天机器人 社会化媒体 新闻 社会研究 社会学 心理学 公共关系 社会心理学 数据科学 计算机科学 政治学 媒体研究 万维网 社会科学
作者
Shuyi Pan,Jie Cui,Yi Mou
出处
期刊:International Journal of Human-computer Interaction [Taylor & Francis]
卷期号:40 (20): 6545-6555 被引量:16
标识
DOI:10.1080/10447318.2023.2256554
摘要

AbstractPresent-day power users of AI-powered social chatbots encounter various uncertainties and concerns when forming relationships with these virtual agents. To provide a systematic analysis of users' concerns and to complement the current West-dominated approach to chatbot studies, we conducted a thorough observation of the experienced uncertainties users reported in a Chinese online community on social chatbots. The results revealed four typical uncertainties: technical uncertainty, relational uncertainty, ontological uncertainty, and sexual uncertainty. We further conducted visibility and sentiment analysis to capture users' response patterns toward various uncertainties. We discovered that users' identification of social chatbots is dynamic and contextual. Our study contributes to expanding, summarizing, and elucidating users' experienced uncertainties and concerns as they form intimate relationships with AI agents.Keywords: AIsocial chatbotReplikahuman-chatbot relationshipuncertainty Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsShuyi PanShuyi Pan is a Ph.D. student in the School of Media and Communication at Shanghai Jiao Tong University and a visiting researcher at Utrecht University. Her research interests include social chatbot, human-AI relationship, and gender.Jie CuiJie Cui is a Ph.D. student in journalism and communication at the School of Media & Communication (SMC), Shanghai Jiao Tong University. Her research interests include political communication and popular culture.Yi MouYi Mou is an associate professor in the School of Media and Communication at Shanghai Jiao Tong University. Her research interests include new media study and human–machine communication.
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