Studies of Online Cohorts for Internalizing symptoms and Language (SOCIAL) I and II: Baseline assessments and rationale

Background: Internalizing, externalizing, and somatoform disorders are the most common and disabling forms of psychopathology. Our understanding of these clinical problems is limited by a reliance on self-report along with research using small samples. Social media has emerged as an exciting venue in which to collect large sample of longitudinal data from individuals to study psychopathology. We report the results of two large ongoing studies in which we collect Twitter data and self-reported clinical screening scales, the Studies of Online Cohorts for Internalizing symptoms and Language (SOCIAL). Methods: Participants were a sample of Twitter-using adults (SOCIAL-I: N= 1123) targeted to be nationally representative in terms of age, sex assigned at birth, and race/ethnicity as well as a sample of college students in the Midwest (SOCIAL-II: N=1988), of which 61% were Twitter users. For all participants who were Twitter users, we asked for access to their Twitter handle which we analyzed with Botometer, an online application rating the likelihood the account belongs to a bot. We divided participants into four groups: 1) Twitter users who did not give us their handle or gave us invalid handles (“Invalid”), 2) those who denied being Twitter users (“No Twitter,” only available for SOCIAL-II), 3) Twitter users who gave their handles but whose account had high bot score (“Bot-like”), and 4) Twitter users who provided their handles and had low bot scores (“Valid”). We explore whether there are significant differences between these groups in terms of their sociodemographic features, clinical symptoms, and aspects of social media use (i.e., platforms used and time). Results: In SOCIAL-I, most individuals were classified as valid (n=580) and few were deemed bot-like (n=190). 353 gave no handle. In SOCIAL-II, many individuals were not Twitter users (n = 760). Of the Twitter users in SOCIAL-II (n = 1, 455), most were classified as either invalid (n = 515) or valid (n = 484), with a smaller fraction deemed bot-like (n = 229). Participants reported high rates of mental health diagnoses as well as high level of symptoms, especially in SOCIAL-II. In general, differences between individuals who provided or did not provide their social media handles were small and not statistically significant. Conclusions: Triangulating passively-acquired social media data and self-reported questionnaires offers new possibilities for large-scale assessment and evaluation of vulnerability to mental disorders. The propensity of participants to share social media handles is likely not a source of sample bias in subsequent social media analytics.