Social Media for Mental Health: Data, Methods, and Findings

TL;DR

This paper reviews how social media data and advanced analytical methods are used to understand, detect, and support mental health challenges, highlighting new opportunities for medical practice and policy influence.

Contribution

It provides a comprehensive overview of current methodologies and findings in analyzing social media data for mental health, emphasizing novel data sources and analytical approaches.

Findings

Identification of linguistic, visual, and emotional indicators of mental health issues

Survey of machine learning and natural language processing techniques used

Discussion of future research directions in social media for mental health

Abstract

There is an increasing number of virtual communities and forums available on the web. With social media, people can freely communicate and share their thoughts, ask personal questions, and seek peer-support, especially those with conditions that are highly stigmatized, without revealing personal identity. We study the state-of-the-art research methodologies and findings on mental health challenges like depression, anxiety, suicidal thoughts, from the pervasive use of social media data. We also discuss how these novel thinking and approaches can help to raise awareness of mental health issues in an unprecedented way. Specifically, this chapter describes linguistic, visual, and emotional indicators expressed in user disclosures. The main goal of this chapter is to show how this new source of data can be tapped to improve medical practice, provide timely support, and influence government or policymakers. In the context of social media for mental health issues, this chapter categorizes social media data used, introduces different deployed machine learning, feature engineering, natural language processing, and surveys methods and outlines directions for future research.