Helping or Hurting? Predicting Changes in Users' Risk of Self-Harm Through Online Community Interactions
This addresses the risk of self-harm for users in online support communities, but it is incremental as it applies an existing classification method to a new dataset.
The paper tackled the problem of predicting whether interactions in online self-harm prevention communities help or harm distressed users, achieving a macro-F1 score of up to 0.69.
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.