# Engagement Patterns of Peer-to-Peer Interactions on Mental Health Platforms

arXiv: https://arxiv.org/abs/2004.04999 · HTML: https://mlsanity.com/paper/2004.04999/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Apr 2020.

## Built on by (most cited first, 16 of 16)

- [Cognitive Reframing of Negative Thoughts through Human-Language Model Interaction](https://mlsanity.com/paper/2305.02466/) (2023)
- [Towards Facilitating Empathic Conversations in Online Mental Health Support: A Reinforcement Learning Approach](https://mlsanity.com/paper/2101.07714/) (2021)
- [Facilitating Self-Guided Mental Health Interventions Through Human-Language Model Interaction: A Case Study of Cognitive Restructuring](https://mlsanity.com/paper/2310.15461/) (2023)
- [PATIENT-Psi: Using Large Language Models to Simulate Patients for Training Mental Health Professionals](https://mlsanity.com/paper/2405.19660/) (2024)
- [Boosting Distress Support Dialogue Responses with Motivational Interviewing Strategy](https://mlsanity.com/paper/2305.10195/) (2023)
- [IMBUE: Improving Interpersonal Effectiveness through Simulation and Just-in-time Feedback with Human-Language Model Interaction](https://mlsanity.com/paper/2402.12556/) (2024)
- [Exploring Self-Identified Counseling Expertise in Online Support Forums](https://mlsanity.com/paper/2106.12976/) (2021)
- [Critical Behavioral Traits Foster Peer Engagement in Online Mental Health Communities](https://mlsanity.com/paper/2309.01618/) (2023)
- [Characterizing Information Seeking Events in Health-Related Social Discourse](https://mlsanity.com/paper/2308.09156/) (2023)
- [Creation and evaluation of timelines for longitudinal user posts](https://mlsanity.com/paper/2303.05891/) (2023)
- [RedditESS: A Mental Health Social Support Interaction Dataset -- Understanding Effective Social Support to Refine AI-Driven Support Tools](https://mlsanity.com/paper/2503.21888/) (2025)
- [Do LLMs Find Human Answers To Fact-Driven Questions Perplexing? A Case Study on Reddit](https://mlsanity.com/paper/2404.01147/) (2024)
- [Mixed Chain-of-Psychotherapies for Emotional Support Chatbot](https://mlsanity.com/paper/2409.19533/) (2024)
- [Sentiment Analysis of the COVID-related r/Depression Posts](https://mlsanity.com/paper/2108.06215/) (2021)
- [Assess and Prompt: A Generative RL Framework for Improving Engagement in Online Mental Health Communities](https://mlsanity.com/paper/2508.16788/) (2025)
- [Stigma and Support in Online Sexual Violence Narratives on Reddit](https://mlsanity.com/paper/2608.11433/) (2026)

## Abstract (arXiv)

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.