Cited by 643Mental Health via WritingEmotion and Mood RecognitionSentiment Analysis and Opinion Mining

Facebook language predicts depression in medical records

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Eichstaedt, Johannes C., Smith, Robert J., Merchant, Raina M., Ungar, Lyle H., Crutchley, Patrick, Preoţiuc-Pietro, Daniel, Asch, David A., Schwartz, H. Andrew·2018·Proceedings of the National Academy of Sciences

Abstract

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.

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  1. [33]Ringeval F 2017 AVEC 2017: Real-life depression, and affect recognition workshop and challenge Proceedings of the 7th Annual Workshop on Audio/Visual Emotion Challenge Association for Computing Machinery New York 3 9

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Authors
Eichstaedt, J. C., Smith, R. J., Merchant, R. M., Ungar, L. H., Crutchley, P., Preoţiuc-Pietro, D., Asch, D. A., & Schwartz, H. A.
Affiliations
  • University of Pennsylvania (US)
  • University of Pennsylvania Health System (US)
  • Positive Psychology Center, University of Pennsylvania, Philadelphia, PA 19104;
  • Philadelphia VA Medical Center (US)
  • Stony Brook University (US)
Corresponding author
Eichstaedt, Johannes C.
Venue
Proceedings of the National Academy of Sciences
Published
2018-10-15
Volume
115
Issue
44
Pages
11203-11208
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
OpenAlex
W2897583329
Cited by
643
References
46
Citations by year
2026: 64 · 2025: 68 · 2024: 77 · 2023: 87 · 2022: 107 · 2021: 111 · 2020: 84 · 2019: 41 · 2018: 3
FWCI
80.90
Type
journal article
Language
en
Open access
Locations
  • Proceedings of the National Academy of Sciences (publishedVersion, cc-by-nc-nd)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
Funders
  • Robert Wood Johnson Foundation (Pioneer Award)
  • Templeton Religion Trust (TRT0048)
  • HHS | NIH | National Heart, Lung, and Blood Institute (HL122457)
Topics
  • Social Sciences › Psychology › Social Psychology › Mental Health via Writing
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
  • Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
Keywords
depression, Facebook posts, social media language, depression prediction, electronic health records, depression screening, emergency department patients, rumination, loneliness, sadness, self-absorption, hostility
MeSH terms
Adult, Depression, Depressive Disorder, Female, Humans, Language, Male, Surveys and Questionnaires, Electronic Health Records, Social Media

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