Online mental health support communities have grown in recent years for providing accessible mental and emotional health support through volunteer counselors. Despite millions of people participating in chat support on these platforms, the clinical effectiveness of these communities on mental health symptoms remains unknown. Furthermore, although volunteers receive some training based on established therapeutic skills studied in face-to-face environments such as active listening and motivational interviewing, it remains understudied how the usage of these skills in this online context affects people's mental health status. In our work, we collaborate with one of the largest online peer support platforms and use both natural language processing and machine learning techniques to measure how one-on-one support chats affect depression and anxiety symptoms. We measure how the techniques and characteristics of support providers, such as using affirmation, empathy, and past experience on the platform, affect support-seekers' mental health changes. We find that online peer support chats improve both depression and anxiety symptoms with a statistically significant but relatively small effect size. Additionally, support providers' techniques such as emphasizing the autonomy of the client lead to better mental health outcomes. However, we also found that some behaviors (e.g. persuading) are actually harmful to depression and anxiety outcomes. Our work provides key understanding for mental health care in the online setting and designing training systems for online support providers.

翻译：近年来，在线心理健康支持社区通过志愿咨询师提供可及的心理与情绪健康支持，已获得显著发展。尽管数百万用户参与这些平台的聊天支持，但这些社区对心理健康症状的临床有效性仍属未知。此外，虽然志愿者基于面对面环境中已证明的治疗技能（如主动倾听与动机性访谈）接受培训，但在线情境下这些技能的使用如何影响人们心理健康状态的研究仍显不足。本研究与最大的在线同伴支持平台之一合作，运用自然语言处理与机器学习技术，测量一对一支持聊天对抑郁与焦虑症状的影响。我们测量支持提供者的技巧与特征（如肯定、共情及平台过往经验）对求助者心理变化的作用。研究发现，在线同伴支持聊天对抑郁与焦虑症状具有统计显著但效应量较小的改善作用。此外，支持提供者强调求助者自主性等技巧可带来更优心理健康结局。然而，部分行为（如说服）反而对抑郁与焦虑症状产生负面影响。本研究为在线情境下的心理健康护理及支持提供者培训系统设计提供了关键认知。