Mental health is a significant and growing public health concern. As language usage can be leveraged to obtain crucial insights into mental health conditions, there is a need for large-scale, labeled, mental health-related datasets of users who have been diagnosed with one or more of such conditions. In this paper, we investigate the creation of high-precision patterns to identify self-reported diagnoses of nine different mental health conditions, and obtain high-quality labeled data without the need for manual labelling. We introduce the SMHD (Self-reported Mental Health Diagnoses) dataset and make it available. SMHD is a novel large dataset of social media posts from users with one or multiple mental health conditions along with matched control users. We examine distinctions in users' language, as measured by linguistic and psychological variables. We further explore text classification methods to identify individuals with mental conditions through their language.

通过检查精确模式识别九种不同精神健康状况的自我报告诊断，并使用语言分析和心理测量学变量，本文探讨了通过文本分类法识别患有精神疾病的个体。同时，介绍了SMHD (自我报告精神健康诊断)数据集的构建和分析结果。

SMHD：一个大规模资源，可用于探索多种心理健康状况的在线语言使用