  Mental illness is a global health problem, but access to mental healthcare
resources remain poor worldwide. Online peer-to-peer support platforms attempt
to alleviate this fundamental gap by enabling those who struggle with mental
illness to provide and receive social support from their peers. However,
successful social support requires users to engage with each other and failures
may have serious consequences for users in need. Our understanding of
engagement patterns on mental health platforms is limited but critical to
inform the role, limitations, and design of these platforms. Here, we present a
large-scale analysis of engagement patterns of 35 million posts on two popular
online mental health platforms, TalkLife and Reddit. Leveraging communication
models in human-computer interaction and communication theory, we
operationalize a set of four engagement indicators based on attention and
interaction. We then propose a generative model to jointly model these
indicators of engagement, the output of which is synthesized into a novel set
of eleven distinct, interpretable patterns. We demonstrate that this framework
of engagement patterns enables informative evaluations and analysis of online
support platforms. Specifically, we find that mutual back-and-forth
interactions are associated with significantly higher user retention rates on
TalkLife. Such back-and-forth interactions, in turn, are associated with early
response times and the sentiment of posts.
