Citation

Skaik R, Inkpen D. ACM Computing Surveys 2021; 53(6).

Copyright

(Copyright © 2021)

DOI

10.1145/3422824

PMID

unavailable

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. © 2020 ACM.

Language: en

Keywords

Monitoring; Data collection; Decision making; Mental disorders; Mental health; Natural language processing systems; Social networking (online); social media; NAtural language processing; Best practices; Social media datum; User information; Decision makers; Recent trends