Article2020

Using Social Media for Mental Health Surveillance

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Mental Health via Writing Sentiment Analysis and Opinion Mining Data-Driven Disease Surveillance

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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References

2020The impact of the COVID-19 pandemic on suicide rates 2015A review of depression and suicide risk assessment using speech analysis 2011Harnessing Social Media for Health Promotion and Behavior Change 2014Quantifying Mental Health Signals in Twitter 2017Detecting depression and mental illness on social media: an integrative review 2016Discovering Shifts to Suicidal Ideation from Mental Health Content in Social Media 2016Twitter as a Tool for Health Research: A Systematic Review 2010PRISMA Statement

Cited by

2023Social media and young adults 2023Toward explainable AI (XAI) for mental health detection based on language behavior 2022Early Identification of Depression Severity Levels on Reddit Using Ordinal Classification 2022Machine learning models to detect anxiety and depression through social media: A scoping review 2022Deep learning techniques for suicide and depression detection from online social media: A scoping review 2022Emotion fusion for mental illness detection from social media: A survey

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