ArticleBMC medical informatics and decision making2017
Social media engagement analysis of U.S. Federal health agencies on Facebook.
Article in BMC medical informatics and decision making, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
33 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Evaluating the Effectiveness of Internet-Based Communication for Public Health: Systematic Review.Journal of medical Internet research · 2022Pooled it
- Social Media Use for Health Purposes: Systematic Review.Journal of medical Internet research · 2021Pooled it
- Dental caries misinformation on Instagram: Investigation of sharing and engagement factors.Brazilian oral research · 2026Observational
- Promoting Public Engagement in Palliative and End-of-Life Care Discussions on Chinese Social Media: Model Development and Analysis.Journal of medical Internet research · 2025Article
- Engaging with patients with diabetes: the role of social media in low-income healthcare organisations.BMJ health & care informatics · 2025Article
- Predicting User Engagement in Health Misinformation Correction on Social Media Platforms in Taiwan: Content Analysis and Text Mining Study.Journal of medical Internet research · 2025Article
- Communicating Nutritional Knowledge to the Chinese Public: Examining Predictive Factors of User Engagement on TikTok in China.Behavioral sciences (Basel, Switzerland) · 2024Article
- Exploring motivations for engagement with the Healthy Lunch Box campaign on social media.Health promotion international · 2023Article
- Effects of Chinese provincial CDCs WeChat official account article features on user engagement during the COVID-19 pandemic.Journal of global health · 2023Article
- How Misinformation and Rebuttals in Online Comments Affect People's Intention to Receive COVID-19 Vaccines: The Roles of Psychological Reactance and Misperceptions.Journalism & mass communication quarterly · 2023Article
- Compliance With the US Food and Drug Administration's Guidelines for Health Warning Labels and Engagement in Little Cigar and Cigarillo Content: Computer Vision Analysis of Instagram Posts.JMIR infodemiology · 2023Article
- Public health perinatal promotion during COVID-19 pandemic: a social media analysis.BMC public health · 2022Article
- Identifying features of source and message that influence the retweeting of health information on social media during the COVID-19 pandemic.BMC public health · 2022Article
- The COVID-19 Infodemic Through Facebook: Comparison of Content and the Accuracy of Breastfeeding Information.Cureus · 2022Article
- Use of Live Community Events on Facebook to Share Health and Clinical Research Information With a Minnesota Statewide Community: Exploratory Study.JMIR formative research · 2022Article
- Article
- More collaboration, less seriousness: Investigating new strategies for promoting youth engagement in government-generated videos during the COVID-19 pandemic in China.Computers in human behavior · 2022Article
- Resilience of political leaders and healthcare organizations during COVID-19.PeerJ. Computer science · 2022Article
- The Saudi Ministry of Health's Twitter Communication Strategies and Public Engagement During the COVID-19 Pandemic: Content Analysis Study.JMIR public health and surveillance · 2021Article
- How German health insurance providers use social online networks to promote healthy lifestyles: a content analysis of Facebook® accounts.BMC medical informatics and decision making · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIt is becoming increasingly common for individuals and organizations to use social media platforms such as Facebook. These are being used for a wide variety of purposes including disseminating, discussing and seeking health related information. U.S. Federal health agencies are leveraging these platforms to 'engage' social media users to read, spread, promote and encourage health related discussions. However, different agencies and their communications get varying levels of engagement. In this study we use statistical models to identify factors that associate with engagement.
methodsWe analyze over 45,000 Facebook posts from 72 Facebook accounts belonging to 24 health agencies. Account usage, user activity, sentiment and content of these posts are studied. We use the hurdle regression model to identify factors associated with the level of engagement and Cox proportional hazards model to identify factors associated with duration of engagement.
resultsIn our analysis we find that agencies and accounts vary widely in their usage of social media and activity they generate. Statistical analysis shows, for instance, that Facebook posts with more visual cues such as photos or videos or those which express positive sentiment generate more engagement. We further find that posts on certain topics such as occupation or organizations negatively affect the duration of engagement.
conclusionsWe present the first comprehensive analyses of engagement with U.S. Federal health agencies on Facebook. In addition, we briefly compare and contrast findings from this study to our earlier study with similar focus but on Twitter to show the robustness of our methods.
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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.