Data on social media contain a wealth of user information. Big data research of social media data may also support standard surveillance approaches and provide decision-makers with usable information. These data can be analyzed using Natural Language Processing (NLP) and Machine Learning (ML) techniques to detect signs of mental disorders that need attention, such as depression and suicide ideation. This article presents the recent trends and tools that are used in this field, the different means for data collection, and the current applications of ML and NLP in the surveillance of public mental health. We highlight the best practices and the challenges. Furthermore, we discuss the current gaps that need to be addressed and resolved.

中文翻译：

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使用社交媒体进行心理健康监测

社交媒体上的数据包含丰富的用户信息。社交媒体数据的大数据研究也可以支持标准的监控方法，并为决策者提供有用的信息。这些数据可以使用自然语言处理 (NLP) 和机器学习 (ML) 技术进行分析，以检测需要注意的精神障碍的迹象，例如抑郁症和自杀意念。本文介绍了该领域的最新趋势和工具、数据收集的不同方式以及 ML 和 NLP 在公共心理健康监测中的当前应用。我们强调最佳实践和挑战。此外，我们讨论了当前需要解决和解决的差距。