Covid Connect: Chat-Driven Anonymous Story-Sharing for Peer Support
Christopher M. Collins, Simone Arbour, Nathan E. Beals, Shawn Yama, Jennifer Laffier, Zixin Zhao · Designing Interactive Systems Conference · 2022
The mental-health impact of the Covid-19 pandemic and the related restrictions and isolation have been immense. In this paper, we present a system designed to break down loneliness and isolation, and to allow people to share their stories, complaints, emotions, and gratitude anonymously with one another. Using a chatbot interface to collect visitor stories, and a custom visualization to reveal related past comments from others, Covid Connect links people together through shared pandemic experiences. The collected data also serves to reflect the experiences of the community of participants during the third through fifth waves of the pandemic in the local region. We describe the Covid Connect system, and analyze the collected data for themes and patterns arising from stories shared with the chatbot. Finally, we reflect on the experience through an autobiographical lens, as users of our own system, and posit ideas for the application of similar approaches in other mental health domains.