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

TL;DR

This paper introduces CASS, a neural network-based social-support chatbot architecture designed for online health communities, capable of handling diverse inputs and promoting community engagement.

Contribution

The paper presents a generalizable, neural network-based chatbot architecture (CASS) that supports emotional needs and enhances community engagement in online health forums.

Findings

CASS effectively supports individual emotional needs.

CASS increases overall community engagement.

The architecture is adaptable to various online communities.

Abstract

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.