Anna Fang

Yuhan Liu, Anna Fang, Glen Moriarty et al.

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.

Anna Fang

Poorly designed interventions or those deployed without adequate safeguards can harm the communities they aim to serve, thus exacerbating existing vulnerabilities and leaving individuals unsupported. This is especially the case for the mental health context, where there is a growing trend of relying on technological interventions due to their accessibility and ability to deliver large-scale support. However, the mental health context is also particularly sensitive to change and risks of failure are dire; at their worst, failures in mental health interventions can result in lasting negative outcomes for individuals and tragic losses as people fall through the cracks. Thus, enabling safe ways to experiment in the mental health context is vital to allow both individuals and communities to engage with new interventions without risk of their real-world consequences. Virtual simulation, which uses virtual environments to replicate real-world interactions, processes, and behaviors, offers a promising opportunity for enabling safe, controlled experimentation with its ability to accurately replicate social situations, fears, stressors, and the potential outcomes of specific interactions. This work explores how simulation approaches can support emerging mental health processes through (1) evaluating community-level outcomes using agent-based modeling and (2) individual training in the mental health context through embodied, controlled spaces. I demonstrate this use of virtual simulation systems through a grounded human-centered approach, where system design is guided by empirical understanding of current real-world needs and challenges. By leveraging simulation to create environments where mental health strategies can be safely tested and practiced, this work aims to open new possibilities for designing scalable, user-centered systems that are effective and safe.