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Identifying signals associated with psychiatric illness utilizing language and images posted to Facebook.

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?

SUBMITTER: Birnbaum ML

PROVIDER: S-EPMC7713057 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Identifying signals associated with psychiatric illness utilizing language and images posted to Facebook.

Birnbaum Michael L ML Norel Raquel R Van Meter Anna A Ali Asra F AF Arenare Elizabeth E Eyigoz Elif E Agurto Carla C Germano Nicole N Kane John M JM Cecchi Guillermo A GA

NPJ schizophrenia 20201203 1

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 uploa ...[more]

PMID: 33273468

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