# Exploring the Effects of AI-assisted Emotional Support Processes in Online Mental Health Community

arXiv: https://arxiv.org/abs/2202.10065 · HTML: https://mlsanity.com/paper/2202.10065/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 47 references that are in the map.
- First posted on arXiv 21 Feb 2022.

## Builds on (most cited first, 2 of 2)

- [A Computational Approach to Understanding Empathy Expressed in Text-Based Mental Health Support](https://mlsanity.com/paper/2009.08441/) (2020)
- [Bursts of Activity: Temporal Patterns of Help-Seeking and Support in Online Mental Health Forums](https://mlsanity.com/paper/2004.10330/) (2020)

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

- [Co-Writing with AI, on Human Terms: Aligning Research with User Demands Across the Writing Process](https://mlsanity.com/paper/2504.12488/) (2025)
- [From Solver to Tutor: Evaluating the Pedagogical Intelligence of LLMs with KMP-Bench](https://mlsanity.com/paper/2603.02775/) (2026)
- [Detecting Effects of AI-Mediated Communication on Language Complexity and Sentiment](https://mlsanity.com/paper/2504.19556/) (2025)
- [AnnoSense: A Framework for Physiological Emotion Data Collection in Everyday Settings for AI](https://mlsanity.com/paper/2508.02680/) (2025)

## Abstract (arXiv)

Social support in online mental health communities (OMHCs) is an effective and accessible way of managing mental wellbeing. In this process, sharing emotional supports is considered crucial to the thriving social supports in OMHCs, yet often difficult for both seekers and providers. To support empathetic interactions, we design an AI-infused workflow that allows users to write emotional supporting messages to other users' posts based on the elicitation of the seeker's emotion and contextual keywords from writing. Based on a preliminary user study (N = 10), we identified that the system helped seekers to clarify emotion and describe text concretely while writing a post. Providers could also learn how to react empathetically to the post. Based on these results, we suggest design implications for our proposed system.