Agent-based Simulation for Online Mental Health Matching
This work addresses the challenge of finding suitable interaction partners for users in online mental health communities, which is an incremental improvement over existing mechanisms.
The paper tackled the problem of underdeveloped user matching in online mental health communities by developing an agent-based simulation framework to compare algorithms, finding that the deferred-acceptance algorithm significantly improved support-seeker experiences in one-on-one chats while keeping waiting times low.
Online mental health communities (OMHCs) are an effective and accessible channel to give and receive social support for individuals with mental and emotional issues. However, a key challenge on these platforms is finding suitable partners to interact with given that mechanisms to match users are currently underdeveloped. In this paper, we collaborate with one of the world's largest OMHC to develop an agent-based simulation framework and explore the trade-offs in different matching algorithms. The simulation framework allows us to compare current mechanisms and new algorithmic matching policies on the platform, and observe their differing effects on a variety of outcome metrics. Our findings include that usage of the deferred-acceptance algorithm can significantly better the experiences of support-seekers in one-on-one chats while maintaining low waiting time. We note key design considerations that agent-based modeling reveals in the OMHC context, including the potential benefits of algorithmic matching on marginalized communities.