Employing Social Media to Improve Mental Health Outcomes

TL;DR

This chapter addresses leveraging social media to detect, predict, and manage mental health outcomes, confronting the challenge of under-treated mental illness and the need for timely interventions. It maps three research thrusts: detecting and predicting mental health states from social media signals, translating these signals into real-world practice through participatory and clinically aligned frameworks, and addressing ethics, privacy, and societal impact. Key contributions include evaluating validity and generalizability of social-media–based predictions, integrating multimodal data for personalized assessments, applying causal inference to linguistic signals, and demonstrating real-world applications such as psychotic-relapse forecasting and near real-time nationwide suicide surveillance. The work advocates human-centered, interdisciplinary collaboration to harness benefits while mitigating harms and inequities, framing the endeavor as a socio-technical problem requiring careful governance and user-centered design.

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.