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.

翻译：在线心理健康社区（OMHCs）是为存在心理与情绪问题的个体提供和获取社会支持的有效且便捷的渠道。然而，这些平台面临的关键挑战在于，由于用户匹配机制尚不完善，难以找到合适的互动对象。本文与全球最大的OMHC之一合作，开发了一个基于智能体的仿真框架，并探讨了不同匹配算法之间的权衡。该仿真框架使我们能够比较平台现有机制与新型算法匹配策略，观察它们对多种结果指标的不同影响。研究发现，使用延迟接受算法能够显著改善一对一聊天中寻求支持者的体验，同时保持较低等待时间。我们指出了基于智能体的建模在OMHC情境下揭示的关键设计考量，包括算法匹配对边缘化社区的潜在益处。