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Identifying signals associated with psychiatric illness utilizing language and images posted to Facebook
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Birnbaum, Michael L.; Norel, Raquel; Van Meter, Anna; Ali, Asra F.; Arenare, Elizabeth; Eyigoz, Elif; Agurto, Carla; Germano, Nicole; Kane, John M.; Cecchi, Guillermo A.
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In: NPJ Schizophr (2020)
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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.
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Keyword:
Article
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URL: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7713057/
https://doi.org/10.1038/s41537-020-00125-0
http://www.ncbi.nlm.nih.gov/pubmed/33273468
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Language as a Biomarker for Psychosis: A Natural Language Processing Approach
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In: Schizophr Res (2020)
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Detection of acute 3,4-methylenedioxymethamphetamine (MDMA) effects across protocols using automated natural language processing
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Differential 28-Days Cyclic Modulation of Affective Intensity in Female and Male Participants via Social Media
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Prediction of psychosis across protocols and risk cohorts using automated language analysis
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Predicting natural language descriptions of mono-molecular odorants
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Fast Distributed Dynamics of Semantic Networks via Social Media
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A Window into the Intoxicated Mind? Speech as an Index of Psychoactive Drug Effects
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