  Recent years have seen a rise in social media platforms that provide
peer-to-peer support to individuals suffering from mental distress. Studies on
the impact of these platforms have focused on either short-term scales of
single-post threads, or long-term changes over arbitrary period of time (months
or years). While important, such arbitrary periods do not necessarily follow
users' progressions through acute periods of distress. Using data from
Talklife, a mental health platform, we find that user activity follows a
distinct pattern of high activity periods with interleaving periods of no
activity, and propose a method for identifying such bursts and breaks in
activity. We then show how studying activity during bursts can provide a
personalized, medium-term analysis for a key question in online mental health
communities: What characteristics of user activity lead some users to find
support and help, while others fall short? Using two independent outcome
metrics, moments of cognitive change and self-reported changes in mood during a
burst of activity, we identify two actionable features that can improve
outcomes for users: persistence within bursts, and giving complex emotional
support to others. Our results demonstrate the value of considering bursts as a
natural unit of analysis for psychosocial change in online mental health
communities.
