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.

本文提出了一种神经框架来支持研究在线支持社区和 Twitter、Reddit 等一般社区的用户，介绍了一种鉴别自伤风险发帖的方法，并展示了在 RSDD 一般论坛数据集上通过自然语言分析识别出抑郁症用户的方法优于强基线。