  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.
