Ruba Skaik
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Abstract
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.
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