ArticleJournal of medical Internet research2015
A new source of data for public health surveillance: Facebook likes.
Article in Journal of medical Internet research, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers, 2 of them syntheses that pooled it.
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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
56 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Social media based surveillance systems for healthcare using machine learning: A systematic review.Journal of biomedical informatics · 2020Pooled it
- The Application of Internet-Based Sources for Public Health Surveillance (Infoveillance): Systematic Review.Journal of medical Internet research · 2020Pooled it
- Leveraging social media data to study disease and treatment characteristics of Hodgkin's lymphoma Using Natural Language Processing methods.PLOS digital health · 2025Article
- Improving the communication of uncertainty in food safety on social media: a content analysis of aspartame risk messages.Frontiers in public health · 2025Article
- Internet-based surveillance to track trends in seasonal allergies across the United States.PNAS nexus · 2024Article
- Digital epidemiology: harnessing big data for early detection and monitoring of viral outbreaks.Infection prevention in practice · 2024Review
- Article
- Using COVID-19 Vaccine Attitudes on Twitter to Improve Vaccine Uptake Forecast Models in the United States: Infodemiology Study of Tweets.JMIR infodemiology · 2023Article
- From pandemic to Plandemic: Examining the amplification and attenuation of COVID-19 misinformation on social media.Social science & medicine (1982) · 2023Article
- Reaching Populations at Risk for HIV Through Targeted Facebook Advertisements: Cost-Consequence Analysis.JMIR formative research · 2023Article
- Impact of COVID-19 Vaccine Misinformation on Social Media Virality: Content Analysis of Message Themes and Writing Strategies.Journal of medical Internet research · 2022Article
- Simulating Facebook Advertisements to Establish Cost per New HIV Diagnosis Using Routine and Targeted Models in a Local Population.Healthcare (Basel, Switzerland) · 2022Article
- How Italy Tweeted about COVID-19: Detecting Reactions to the Pandemic from Social Media.International journal of environmental research and public health · 2022Article
- Using Facebook language to predict and describe excessive alcohol use.Alcoholism, clinical and experimental research · 2022Article
- Engaging social media users with attitudinal messages during health crisis communication.Lingua. International review of general linguistics. Revue internationale de linguistique generale · 2022Article
- How News Agencies' Twitter Posts on COVID-19 Vaccines Attract Audiences' Twitter Engagement: A Content Analysis.International journal of environmental research and public health · 2022Article
- JUE Insight: The geographic spread of COVID-19 correlates with the structure of social networks as measured by Facebook.Journal of urban economics · 2022Article
- Profile Pictures in the Digital World: Self-Photographs Predict Better Life Satisfaction.International journal of environmental research and public health · 2021Article
- Harnessing Social Media in the Modelling of Pandemics-Challenges and Opportunities.Bulletin of mathematical biology · 2021Article
- Insights From the SmokeFree.gov Initiative Regarding the Use of Smoking Cessation Digital Platforms During the COVID-19 Pandemic: Cross-sectional Trends Analysis Study.Journal of medical Internet research · 2021Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundInvestigation into personal health has become focused on conditions at an increasingly local level, while response rates have declined and complicated the process of collecting data at an individual level. Simultaneously, social media data have exploded in availability and have been shown to correlate with the prevalence of certain health conditions.
objectiveFacebook likes may be a source of digital data that can complement traditional public health surveillance systems and provide data at a local level. We explored the use of Facebook likes as potential predictors of health outcomes and their behavioral determinants.
methodsWe performed principal components and regression analyses to examine the predictive qualities of Facebook likes with regard to mortality, diseases, and lifestyle behaviors in 214 counties across the United States and 61 of 67 counties in Florida. These results were compared with those obtainable from a demographic model. Health data were obtained from both the 2010 and 2011 Behavioral Risk Factor Surveillance System (BRFSS) and mortality data were obtained from the National Vital Statistics System.
resultsFacebook likes added significant value in predicting most examined health outcomes and behaviors even when controlling for age, race, and socioeconomic status, with model fit improvements (adjusted R(2)) of an average of 58% across models for 13 different health-related metrics over basic sociodemographic models. Small area data were not available in sufficient abundance to test the accuracy of the model in estimating health conditions in less populated markets, but initial analysis using data from Florida showed a strong model fit for obesity data (adjusted R(2)=.77).
conclusionsFacebook likes provide estimates for examined health outcomes and health behaviors that are comparable to those obtained from the BRFSS. Online sources may provide more reliable, timely, and cost-effective county-level data than that obtainable from traditional public health surveillance systems as well as serve as an adjunct to those systems.
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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.