Facebook language predicts depression in medical records
Open accessAbstract
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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Prior work has found that depressed individuals use words related to sadness and somatic health concerns (health and biology words).
“These include words related to sadness, as well as words related to somatic health concerns (health and biology words, with the biology category combining body, health, sexual, and ingestion words) [22,23].”
From Behavioral Activation and Depression Symptomatology: Longitudinal Assessment of Linguistic Indicators in Text-Based Therapy Sessions (2021) · cites this paper as [23]
Facebook developed machine learning algorithms that record what people like and how long they hover over posts, and textmining enables sentiment analysis.
“Over the years, Facebook developed machine learning algorithms studying the behavior of their users in detail on the platform. In order to get to know their users better, they do not only record what people ‘like’ (i.e., give a ‘thumbs up’) but also how long they hover over a certain post. This could be interpreted as showing a special interest in a certain area. ‘Textmining’ enables Facebook & Co”
From Addictive Features of Social Media/Messenger Platforms and Freemium Games against the Background of Psychological and Economic Theories (2019) · cites this paper as [67]
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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.
- 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
- 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
- hybrid
- doi.org
- 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
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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