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

TL;DR

This paper investigates how online community interactions influence the mental health of users seeking support for self-harm, developing a classifier to predict whether interactions help or harm users, with promising accuracy.

Contribution

It introduces a dataset of online support conversations and presents a classifier to predict the impact of interactions on users' mental states.

Findings

Classifier achieves macro-F1 score of up to 0.69

Interactions can both help and harm users seeking support

Dataset enables future research on online mental health support

Abstract

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.