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Facebook language predicts depression in medical records

Eichstaedt, Johannes C ; Smith, Robert J ; Merchant, Raina M ; Ungar, Lyle H ; Crutchley, Patrick ; Preoţiuc-Pietro, Daniel ; Asch, David A ; Schwartz, H. Andrew

Proceedings of the National Academy of Sciences of the United States of America, 2018, Vol.115(44), p.11203-11208 [Peer Reviewed Journal]

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  • Title:
    Facebook language predicts depression in medical records
  • Author/Creator: Eichstaedt, Johannes C ; Smith, Robert J ; Merchant, Raina M ; Ungar, Lyle H ; Crutchley, Patrick ; Preoţiuc-Pietro, Daniel ; Asch, David A ; Schwartz, H. Andrew
  • Subjects: Social Sciences ; Big Data ; Depression ; Social Media ; Facebook ; Screening
  • Is Part Of: Proceedings of the National Academy of Sciences of the United States of America, 2018, Vol.115(44), p.11203-11208
  • Description: Significance Depression is disabling and treatable, but underdiagnosed. In this study, we show that the content shared by consenting users on Facebook can predict a future occurrence of depression in their medical records. Language predictive of depression includes references to typical symptoms, including sadness, loneliness, hostility, rumination, and increased self-reference. This study suggests that an analysis of social media data could be used to screen consenting individuals for depression. Further, social media content may point clinicians to specific symptoms of depression. Depression, the most prevalent mental illness, is underdiagnosed and undertreated, highlighting the need to extend the scope of current screening methods. Here, we use language from Facebook posts of consenting individuals to predict depression recorded in electronic medical records. We accessed the history of Facebook statuses posted by 683 patients visiting a large urban academic emergency department, 114 of whom had a diagnosis of depression in their medical records. Using only the language preceding their first documentation of a diagnosis of depression, we could identify depressed patients with fair accuracy [area under the curve (AUC) = 0.69], approximately matching the accuracy of screening surveys benchmarked against medical records. Restricting Facebook data to only the 6 months immediately preceding the first documented diagnosis of depression yielded a higher prediction accuracy (AUC = 0.72) for those users who had sufficient Facebook data. Significant prediction of future depression status was possible as far as 3 months before its first documentation. We found that language predictors of depression include emotional (sadness), interpersonal (loneliness, hostility), and cognitive (preoccupation with the self, rumination) processes. Unobtrusive depression assessment through social media of consenting individuals may become feasible as a scalable complement to existing screening and monitoring procedures.
  • Identifier: ISSN: 0027-8424 ; E-ISSN: 1091-6490 ; DOI: 10.1073/pnas.1802331115 ; PMCID: 6217418 ; PMID: 30322910