  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.
