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

The DOI matches its Crossref record; 45 of the 46 references it cites were found in a registry or an index.

Facebook language predicts depression in medical records

Open access
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
DOI 10.1073/pnas.1802331115PMC PMC6217418
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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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Crossref · 10.1073/pnas.1802331115

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Fig. 1. Prediction performances of future diagnosis of depression in the EMR based on demographics and Facebook posting activity, reported as cross-validated out-of-sample AUCs.
Fig. 1. Prediction performances of future diagnosis of depression in the EMR based on demographics and Facebook posting activity, reported as cross-validated out-of-sample AUCs.CC BY-NC-ND · PubMed Central
Fig. 2. ROC curve for a Facebook activity-based prediction model (all predictors combined; blue), and points as combinations of true and false positive rates reported by Noyes et al. (17) for different combinations of depression surveys (a and b, 9-item Mini-International Neuropsychiatric Interview–Major Depressive Episode Module; c and d, 15-item Geriatric Depression Scale with a cutoff >6) and time windows in Medicare claims data (a and c, within 6 mo before and after survey; b and d, within 12 mo).
Fig. 2. ROC curve for a Facebook activity-based prediction model (all predictors combined; blue), and points as combinations of true and false positive rates reported by Noyes et al. (17) for different combinations of depression surveys (a and b, 9-item Mini-International Neuropsychiatric Interview–Major Depressive Episode Module; c and d, 15-item Geriatric Depression Scale with a cutoff >6) and time windows in Medicare claims data (a and c, within 6 mo before and after survey; b and d, within 12 mo).CC BY-NC-ND · PubMed Central
Fig. 3. AUC prediction accuracies of future depression status as a function of time before the documentation of depression in the medical record. Shown in blue are the 6-mo time windows of Facebook data used for the predictions; the blue dots indicate the AUCs obtained for these windows. Error bars indicate SEs (based on the 10 cross-validation folds). Logarithmic trendline is shown to guide the eye.
Fig. 3. AUC prediction accuracies of future depression status as a function of time before the documentation of depression in the medical record. Shown in blue are the 6-mo time windows of Facebook data used for the predictions; the blue dots indicate the AUCs obtained for these windows. Error bars indicate SEs (based on the 10 cross-validation folds). Logarithmic trendline is shown to guide the eye.CC BY-NC-ND · PubMed Central
Fig. 4. Ten language topics most positively associated with a future depression diagnosis controlling for demographics (*P < 0.05, **P < 0.01, and ***P < 0.001; BHP < 0.05 after Benjamini–Hochberg correction for multiple comparisons). Font size reflects relative prevalence of words within topics. Color shading is to aid readability and carries no meaning.
Fig. 4. Ten language topics most positively associated with a future depression diagnosis controlling for demographics (*P < 0.05, **P < 0.01, and ***P < 0.001; BHP < 0.05 after Benjamini–Hochberg correction for multiple comparisons). Font size reflects relative prevalence of words within topics. Color shading is to aid readability and carries no meaning.CC BY-NC-ND · PubMed Central

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Metadata

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.
ORCID
Venue
Proceedings of the National Academy of Sciences
Publisher
National Academy of Sciences
Published
2018-10-15
Volume
115
Issue
44
Pages
11203-11208
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
DOI
10.1073/pnas.1802331115
PMC
PMC6217418
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
Licence
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
  • Physical Sciences › Computer Science › Artificial Intelligence › 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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