Depression and Self-Harm Risk Assessment in Online Forums

TL;DR

This paper introduces a neural framework for detecting self-harm risk and depression in online forums, outperforming previous methods and providing a large dataset for depression detection.

Contribution

The paper presents a novel neural approach for identifying self-harm risk and depression in online communities, along with a large-scale dataset for depression analysis.

Findings

Our method outperforms previous approaches in identifying self-harm posts.

The approach effectively detects depressed users based on language use.

The large-scale RSDD dataset enables better depression detection models.

Abstract

Users suffering from mental health conditions often turn to online resources for support, including specialized online support communities or general communities such as Twitter and Reddit. In this work, we present a neural framework for supporting and studying users in both types of communities. We propose methods for identifying posts in support communities that may indicate a risk of self-harm, and demonstrate that our approach outperforms strong previously proposed methods for identifying such posts. Self-harm is closely related to depression, which makes identifying depressed users on general forums a crucial related task. We introduce a large-scale general forum dataset ("RSDD") consisting of users with self-reported depression diagnoses matched with control users. We show how our method can be applied to effectively identify depressed users from their use of language alone. We demonstrate that our method outperforms strong baselines on this general forum dataset.