Abstract
Prior research has identified associations between social media activity and psychiatric diagnoses; however, diagnoses are rarely clinically confirmed. Toward the goal of applying novel approaches to improve outcomes, research using real patient data is necessary. We collected 3,404,959 Facebook messages and 142,390 images across 223 participants (mean age = 23.7; 41.7% male) with schizophrenia spectrum disorders (SSD), mood disorders (MD), and healthy volunteers (HV). We analyzed features uploaded up to 18 months before the first hospitalization using machine learning and built classifiers that distinguished SSD and MD from HV, and SSD from MD. Classification achieved AUC of 0.77 (HV vs. MD), 0.76 (HV vs. SSD), and 0.72 (SSD vs. MD). SSD used more (P < 0.01) perception words (hear, see, feel) than MD or HV. SSD and MD used more (P < 0.01) swear words compared to HV. SSD were more likely to express negative emotions compared to HV (P < 0.01). MD used more words related to biological processes (blood/pain) compared to HV (P < 0.01). The height and width of photos posted by SSD and MD were smaller (P < 0.01) than HV. MD photos contained more blues and less yellows (P < 0.01). Closer to hospitalization, use of punctuation increased (SSD vs HV), use of negative emotion words increased (MD vs. HV), and use of swear words increased (P < 0.01) for SSD and MD compared to HV. Machine-learning algorithms are capable of differentiating SSD and MD using Facebook activity alone over a year in advance of hospitalization. Integrating Facebook data with clinical information could one day serve to inform clinical decision-making.
Highlights
Mental illness occurs in ~20% of the population worldwide[1,2] and can be associated with a significant individual, familial, and societal burden[3,4,5]
Image features Compared to healthy volunteers (HV), both height and width of photos posted by spectrum disorders (SSD) or mood disorders (MD), were significantly smaller (P < 0.01)
Words related to biological processes as well as first person pronouns were used more often by MD compared to HV (P < 0.01)
Summary
Mental illness occurs in ~20% of the population worldwide[1,2] and can be associated with a significant individual, familial, and societal burden[3,4,5]. A growing body of literature describes linguistic and behavioral patterns, extracted from social media sites like Facebook and Twitter, that are associated with psychiatric diagnoses and symptoms[10,11,12,13,14,15,16,17,18,19]. Most have utilized a computational approach to flag publicly available social media data from profiles of users who self-disclose having a diagnosis[27,28,29,30] without the ability to assess whether the individual meets diagnostic criteria for a psychiatric disorder. Toward the goal of applying novel, social media-based approaches to identify and monitor individuals in need of psychiatric care, research using real patient data with clinically confirmed diagnoses is necessary. Over a year in advance of the first psychiatric
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