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arXiv 2101.01583cs.HCcs.LG
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CASS:为在线健康社区构建社会支持聊天机器人

CASS: Towards Building a Social-Support Chatbot for Online Health Community

Institute of Software, Chinese Academy of Sciences(中国科学院软件研究所);University of Chinese Academy of Sciences(中国科学院大学);IBM Research(IBM研究院);The Hong Kong University of Science and Technology(香港科技大学);Pace University(佩斯大学)

Liuping Wang, Dakuo Wang, Feng Tian, Zhenhui Peng, Xiangmin Fan, Zhan Zhang, Shuai Ma, Mo Yu, Xiaojuan Ma, Hongan Wang

摘要:

尽管聊天机器人系统在当今的HCI和CSCW研究中很受欢迎,但由于以下两个原因之一而不足:1)许多系统使用基于规则的对话流,因此它们只能响应有限数量的预定义输入和预先编写的响应;或者2)它们的设计重点是单用户场景,因此不清楚这些系统如何影响其他用户或社区。在本文中,我们开发了一个可推广的聊天机器人架构(CASS),为在线健康社区的社区成员提供社会支持。CASS体系结构基于先进的神经网络算法,因此它可以处理来自用户的新输入,并生成各种响应。CASS也是可推广的,因为它可以很容易地迁移到其他在线社区。通过后续的实地实验,CASS被证明在支持寻求情感支持的个人成员方面是有用的。我们的工作还有助于填补关于聊天机器人如何影响整个社区参与的研究空白。

英文摘要:

Chatbots systems, despite their popularity in today's HCI and CSCW research, fall short for one of the two reasons: 1) many of the systems use a rule-based dialog flow, thus they can only respond to a limited number of pre-defined inputs with pre-scripted responses; or 2) they are designed with a focus on single-user scenarios, thus it is unclear how these systems may affect other users or the community. In this paper, we develop a generalizable chatbot architecture (CASS) to provide social support for community members in an online health community. The CASS architecture is based on advanced neural network algorithms, thus it can handle new inputs from users and generate a variety of responses to them. CASS is also generalizable as it can be easily migrate to other online communities. With a follow-up field experiment, CASS is proven useful in supporting individual members who seek emotional support. Our work also contributes to fill the research gap on how a chatbot may influence the whole community's engagement.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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