# Bursts of Activity: Temporal Patterns of Help-Seeking and Support in Online Mental Health Forums

arXiv: https://arxiv.org/abs/2004.10330 · HTML: https://mlsanity.com/paper/2004.10330/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Apr 2020.

## Built on by (most cited first, 4 of 4)

- [Modeling Motivational Interviewing Strategies On An Online Peer-to-Peer Counseling Platform](https://mlsanity.com/paper/2211.05182/) (2022)
- [Vertical Federated Knowledge Transfer via Representation Distillation for Healthcare Collaboration Networks](https://mlsanity.com/paper/2302.05675/) (2023)
- [Taking a turn for the better: Conversation redirection throughout the course of mental-health therapy](https://mlsanity.com/paper/2410.07147/) (2024)
- [Exploring the Effects of AI-assisted Emotional Support Processes in Online Mental Health Community](https://mlsanity.com/paper/2202.10065/) (2022)

## Abstract (arXiv)

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.