ArticleJournal of medical Internet research2017
Variations in Facebook Posting Patterns Across Validated Patient Health Conditions: A Prospective Cohort Study.
Article in Journal of medical Internet research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 33 citations in OpenAlex.
- A systematic literature review of machine learning in online personal health data.Journal of the American Medical Informatics Association : JAMIA · 2019Pooled it
- Deep Learning for Identification of Alcohol-Related Content on Social Media (Reddit and Twitter): Exploratory Analysis of Alcohol-Related Outcomes.Journal of medical Internet research · 2021Article
- Displayed Depression Symptoms on Facebook at Two Time Points: Content Analysis.JMIR formative research · 2021Article
- Responses to Concerning Posts on Social Media and Their Implications for Suicide Prevention Training for Military Veterans: Qualitative Study.Journal of medical Internet research · 2020Article
- Predicting eating disorders from Internet activity.The International journal of eating disorders · 2020Article
- Article
- Infodemiology and Infoveillance: Scoping Review.Journal of medical Internet research · 2020Article
- Comparison of Social Media, Syndromic Surveillance, and Microbiologic Acute Respiratory Infection Data: Observational Study.JMIR public health and surveillance · 2020Observational
- Incorporating Information From Electronic and Social Media Into Psychiatric and Psychotherapeutic Patient Care: Survey Among Clinicians.Journal of medical Internet research · 2019Article
- Google search histories of patients presenting to an emergency department: an observational study.BMJ open · 2019Observational
- Opioid Discussion in the Twittersphere.Substance use & misuse · 2018Article
- Extracting psychiatric stressors for suicide from social media using deep learning.BMC medical informatics and decision making · 2018Article
- Studying social media language changes associated with pregnancy status, trimester, and parity from medical records.Women's health (London, England)Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundSocial media is emerging as an insightful platform for studying health. To develop targeted health interventions involving social media, we sought to identify the patient demographic and disease predictors of frequency of posting on Facebook.
objectiveThe aims were to explore the language topics correlated with frequency of social media use across a cohort of social media users within a health care setting, evaluate the differences in the quantity of social media postings across individuals with different disease diagnoses, and determine if patients could accurately predict their own levels of social media engagement.
methodsPatients seeking care at a single, academic, urban, tertiary care emergency department from March to October 2014 were queried on their willingness to share data from their Facebook accounts and electronic medical records (EMRs). For each participant, the total content of Facebook posts was extracted. Using the latent Dirichlet allocation natural language processing technique, Facebook language topics were correlated with frequency of Facebook use. The mean number of Facebook posts over 6 months prior to enrollment was then compared across validated health outcomes in the sample.
resultsA total of 695 patients consented to provide access to their EMR and social media data. Significantly correlated language topics among participants with the highest quartile of posts contained health terms, such as "cough," "headaches," and "insomnia." When adjusted for demographics, individuals with a history of depression had significantly higher posts (mean 38, 95% CI 28-50) than individuals without a history of depression (mean 22, 95% CI 19-26, P=.001). Except for depression, across prevalent health outcomes in the sample (hypertension, diabetes, asthma), there were no significant posting differences between individuals with or without each condition.
conclusionsHigh-frequency posters in our sample were more likely to post about health and to have a diagnosis of depression. The direction of causality between depression and social media use requires further evaluation. Our findings suggest that patients with depression may be appropriate targets for health-related interventions on social media.
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