Social media, big data, and mental health: current advances and ethical implications

Abstract

Mental health (including substance abuse) is the fifth greatest contributor to the global burden of disease, with an economic cost estimated to be US $2.5 trillion in 2010, and expected to double by 2030. Developing information systems to support and strengthen population-level mental health monitoring forms a core part of the World Health Organization’s Comprehensive Action Plan 2013–2020. In this paper, we review recent work that utilizes social media “big data” in conjunction with associated technologies like natural language processing and machine learning to address pressing problems in population-level mental health surveillance and research, focusing both on technological advances and core ethical challenges.

Within and Between-Person Differences in Language Used Across Anxiety Support and Neutral Reddit Communities

“…Several studies have investigated anxiety in concert with other disorders (Coppersmith et al, 2014(Coppersmith et al, , 2015Gkotsis et al, 2017), but studies that focus on a single condition more commonly focus on depression (De Choudhury et al, 2013; for a review, see Conway and OConnor, 2016). Worldwide, anxiety is the second most prevalent mental health condition and, among all mental disorders, accounts for the second greatest variance in disability-adjusted life years (Whiteford et al, 2013).…”

Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic

“…Lind et al (2017) offer a comprehensive discussion of crowdsourcing, using CrowdFlower, as a means for obtaining coding of latent constructs in comparison with content analysis. Calvo et al (2017) and Guntuku et al (2017) present reviews of NLP research in which social media are used to identify people with psychological issues who may require intervention, and Conway and O'Connor (2016) provide a shorter survey focused on public health monitoring and ethical issues, highlighting the annual Workshop on Computational Linguistics and Clinical Psychology (CLPsych), initiated in 2014, as a forum for bridging the gap between computer science researchers and mental health clinicians (Resnik et al, 2014). Recent CLPsych shared tasks using data from the ReachOut peer support forums have provided opportunities for exploration of technological approaches to risk assessment and crisis detection (Milne et al, 2016;Milne, 2017); see also .…”

Detecting Recovery Problems Just in Time: Application of Automated Linguistic Analysis and Supervised Machine Learning to an Online Substance Abuse Forum (Preprint)

“…By capturing spontaneous, first-hand accounts of authors' beliefs, feelings, and experiences, text-based messages can offer particularly powerful insights into wellness, including the risk of mental health-related outcomes [15]. For instance, prior research has shown that linguistic qualities such as self-focus (as conveyed in the pronouns used) can distinguish those who will go on to post about suicidal ideation [40], and that negative affective language and swearing can identify individuals who go on to relapse in alcohol recovery [41].…”

“…Our approach builds on the power of natural language as a signal of mental health risk, with linguistic cues being increasingly discernable through computational methods. Over the past several decades, researchers have amassed an extensive body of literature showing the power of language to reveal individuals' psychological traits, thoughts, feelings, and likely behaviors [14], including in social media contexts [15].…”