Online Mental Health Communities (OMHCs), such as Reddit, have witnessed a
surge in popularity as go-to platforms for seeking information and support in
managing mental health needs. Platforms like Reddit offer immediate
interactions with peers, granting users a vital space for seeking mental health
assistance. However, the largely unregulated nature of these platforms
introduces intricate challenges for both users and society at large. This study
explores the factors that drive peer engagement within counseling threads,
aiming to enhance our understanding of this critical phenomenon. We introduce
BeCOPE, a novel behavior encoded Peer counseling dataset comprising over 10,118
posts and 58,279 comments sourced from 21 mental health-specific subreddits.
The dataset is annotated using three major fine-grained behavior labels: (a)
intent, (b) criticism, and (c) readability, along with the emotion labels. Our
analysis indicates the prominence of ``self-criticism'' as the most prevalent
form of criticism expressed by help-seekers, accounting for a significant 43%
of interactions. Intriguingly, we observe that individuals who explicitly
express their need for help are 18.01% more likely to receive assistance
compared to those who present ``surveys'' or engage in ``rants.'' Furthermore,
we highlight the pivotal role of well-articulated problem descriptions, showing
that superior readability effectively doubles the likelihood of receiving the
sought-after support. Our study emphasizes the essential role of OMHCs in
offering personalized guidance and unveils behavior-driven engagement patterns.

通过分析 BeCOPE 数据集中的在线心理健康社区中的同行辅导帖子的行为，研究揭示了在线心理健康社区的关键作用以及自我批评和阅读可理解性对帮助寻求者的互动的影响。

关键行为特质促进在线心理健康社区的同龄人参与

Critical Behavioral Traits Foster Peer Engagement in Online Mental  Health Communities

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

Agent-based Simulation for Online Mental Health Matching

Social support in online mental health communities (OMHCs) is an effective
and accessible way of managing mental wellbeing. In this process, sharing
emotional supports is considered crucial to the thriving social supports in
OMHCs, yet often difficult for both seekers and providers. To support
empathetic interactions, we design an AI-infused workflow that allows users to
write emotional supporting messages to other users' posts based on the
elicitation of the seeker's emotion and contextual keywords from writing. Based
on a preliminary user study (N = 10), we identified that the system helped
seekers to clarify emotion and describe text concretely while writing a post.
Providers could also learn how to react empathetically to the post. Based on
these results, we suggest design implications for our proposed system.

本文通过设计一种 AI 辅助工作流程，根据用户的情感状态和文本关键词，使得在在线心理健康社区中进行情感支持成为可能，并提出了相应的设计建议。