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.

在OMHC平台上，我们合作开发了一个基于代理的模拟框架，并研究了不同匹配算法的权衡。我们发现使用递延接受算法可以显著改善一对一聊天中寻求支持者的体验，同时保持较低的等待时间，并揭示了基于算法匹配在OMHC背景下的关键设计考虑因素，包括在弱势社区中的潜在收益。

基于代理的在线心理健康匹配模拟