Employing Social Media to Improve Mental Health Outcomes

TL;DR

This paper reviews a decade of research on using social media data to detect, predict, and improve mental health outcomes, emphasizing ethical challenges and the need for interdisciplinary approaches.

Contribution

It provides a comprehensive overview of methods, translation paradigms, and ethical considerations in leveraging social media for mental health research.

Findings

Social media data can predict mental health risks.

Translation of algorithms to real-world applications faces ethical hurdles.

Interdisciplinary collaboration is essential for responsible research.

Abstract

As social media platforms are increasingly adopted, the data the data people leave behind is shining new light into our understanding of phenomena, ranging from socio-economic-political events to the spread of infectious diseases. This chapter presents research conducted in the past decade that has harnessed social media data in the service of mental health and well-being. The discussion is organized along three thrusts: a first that highlights how social media data has been utilized to detect and predict risk to varied mental health concerns; a second thrust that focuses on translation paradigms that can enable to use of such social media based algorithms in the real-world; and the final thrust that brings to the fore the ethical considerations and challenges that engender the conduct of this research as well as its translation. The chapter concludes by noting open questions and problems in this emergent area, emphasizing the need for deeper interdisciplinary collaborations and participatory research design, incorporating and centering on human agency, and attention to societal inequities and harms that may result from or be exacerbated in this line of computational social science research.