Title: Depression and Self-Harm Risk Assessment in Online Forums,
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.