Background: Detection of depression gained prominence soon after this troublesome disease emerged as a serious public health concern worldwide. Objective: This systematic review aims to summarize the findings of previous studies concerning applying machine learning (ML) methods to text data from social media to detect depressive symptoms and to suggest directions for future research in this area. Methods: A bibliographic search was conducted for the period of January 1990 to December 2020 in Google Scholar, PubMed, Medline, ERIC, PsycINFO, and BioMed. Two reviewers retrieved and independently assessed the 418 studies consisting of 322 articles identified through database searching and 96 articles identified through other sources; 17 of the studies met the criteria for inclusion. Results: Of the 17 studies, 10 had identified depression based on researcher-inferred mental status, 5 had identified it based on users’ own descriptions of their mental status, and 2 were identified based on community membership. The ML approaches of 13 of the 17 studies were supervised learning approaches, while 3 used unsupervised learning approaches; the remaining 1 study did not describe its ML approach. Challenges in areas such as sampling, optimization of approaches to prediction and their features, generalizability, privacy, and other ethical issues call for further research. Conclusions: ML approaches applied to text data from users on social media can work effectively in depression detection and could serve as complementary tools in public mental health practice.

中文翻译：

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使用机器学习方法检测和测量社交媒体上的抑郁症：系统评价

背景：在这种令人烦恼的疾病成为全球严重的公共卫生问题后不久，对抑郁症的检测就引起了人们的重视。目的：本系统评价旨在总结以往关于将机器学习 (ML) 方法应用于社交媒体文本数据以检测抑郁症状的研究结果，并为该领域的未来研究提出方向。方法：在 Google Scholar、PubMed、Medline、ERIC、PsycINFO 和 BioMed 中进行了 1990 年 1 月至 2020 年 12 月期间的书目搜索。两名审稿人检索并独立评估了 418 项研究，其中包括通过数据库搜索确定的 322 篇文章和通过其他来源确定的 96 篇文章；17 项研究符合纳入标准。结果：在 17 项研究中，10 项根据研究人员推断的精神状态确定抑郁症，5 项根据用户自己对其精神状态的描述确定，2 项根据社区成员身份确定。17 项研究中有 13 项的 ML 方法是监督学习方法，而 3 项使用无监督学习方法；其余 1 项研究没有描述其 ML 方法。抽样、预测方法及其特征的优化、普遍性、隐私和其他伦理问题等领域的挑战需要进一步研究。结论：应用于社交媒体用户文本数据的 ML 方法可以有效地检测抑郁症，并可作为公共心理健康实践的补充工具。