ArticlePloS one2019
Evaluating the predictability of medical conditions from social media posts.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
36 citing papers in PubMed.
- Effect of Integrating Patient-Generated Digital Data Into Mental Health Therapy: A Randomized Controlled Trial.Psychiatric services (Washington, D.C.) · 2023Trial
- Consumer Willingness to Share Personal Digital Information for Health-Related Uses.JAMA network open · 2022Trial
- Digital phenotyping and ASCVD risk: An exploratory cross-sectional analysis using online behavioral data.American heart journal plus : cardiology research and practice · 2026Article
- Integrating Sensory Perception and Wearable Monitoring to Promote Healthy Aging: A New Frontier in Nutritional Personalization.Nutrients · 2026Review
- Pragmatic lessons for harnessing social media for health research through digital data donation post-API access.npj digital public health · 2026Review
- Sentiment trajectory modeling in mental health discussions via transformer-based analysis.Frontiers in artificial intelligence · 2026Article
- Understanding Health-Related Discussions on Reddit: Development of a Topic Assignment Method and Exploratory Analysis.JMIR formative research · 2025Article
- Automatic detection of problem-gambling signs from online texts using large language models.PLOS digital health · 2024Article
- Social networks use in the context of Schizophrenia: a review of the literature.Frontiers in psychiatry · 2024Review
- Affective norms for 501 Chinese words from three emotional dimensions rated by depressive disorder patients.Frontiers in psychiatry · 2024Article
- Large-Scale Web Scraping for Problem Gambling Research: A Case Study of COVID-19 Lockdown Effects in Germany.Journal of gambling studies · 2023Article
- Analyzing text message linguistic features: Do people with depression communicate differently with their close and non-close contacts?Behaviour research and therapy · 2023Review
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- Article
- Consumer Views on Privacy Protections and Sharing of Personal Digital Health Information.JAMA network open · 2023Article
- Artificial intelligence-assisted psychosis risk screening in adolescents: Practices and challenges.World journal of psychiatry · 2022Review
- Analyzing the public sentiment on COVID-19 vaccination in social media: Bangladesh context.Array (New York, N.Y.) · 2022Article
- Novel digital approaches to the assessment of problematic opioid use.BioData mining · 2022Review
- Perspectives of Patients and Therapists on Social Media and Digital Data Use in Mental Health Therapy: Thematic Analysis.JMIR formative research · 2022Article
- The relationship between text message sentiment and self-reported depression.Journal of affective disorders · 2022Observational
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We studied whether medical conditions across 21 broad categories were predictable from social media content across approximately 20 million words written by 999 consenting patients. Facebook language significantly improved upon the prediction accuracy of demographic variables for 18 of the 21 disease categories; it was particularly effective at predicting diabetes and mental health conditions including anxiety, depression and psychoses. Social media data are a quantifiable link into the otherwise elusive daily lives of patients, providing an avenue for study and assessment of behavioral and environmental disease risk factors. Analogous to the genome, social media data linked to medical diagnoses can be banked with patients' consent, and an encoding of social media language can be used as markers of disease risk, serve as a screening tool, and elucidate disease epidemiology. In what we believe to be the first report linking electronic medical record data with social media data from consenting patients, we identified that patients' Facebook status updates can predict many health conditions, suggesting opportunities to use social media data to determine disease onset or exacerbation and to conduct social media-based health interventions.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.