# Helping or Hurting? Predicting Changes in Users' Risk of Self-Harm Through Online Community Interactions

arXiv: https://arxiv.org/abs/1804.07253 · HTML: https://mlsanity.com/paper/1804.07253/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Apr 2018.

## Built on by (most cited first, 1 of 1)

- [MentSum: A Resource for Exploring Summarization of Mental Health Online Posts](https://mlsanity.com/paper/2206.00856/) (2022)

## Abstract (arXiv)

In recent years, online communities have formed around suicide and self-harm prevention. While these communities offer support in moment of crisis, they can also normalize harmful behavior, discourage professional treatment, and instigate suicidal ideation. In this work, we focus on how interaction with others in such a community affects the mental state of users who are seeking support. We first build a dataset of conversation threads between users in a distressed state and community members offering support. We then show how to construct a classifier to predict whether distressed users are helped or harmed by the interactions in the thread, and we achieve a macro-F1 score of up to 0.69.