ArticleJMIR public health and surveillance2021
General Audience Engagement With Antismoking Public Health Messages Across Multiple Social Media Sites: Comparative Analysis.
Article in JMIR public health and surveillance, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 22 papers, 4 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
22 citing papers in PubMed, 4 syntheses or guidelines pooled it, 40 citations in OpenAlex.
- Elements Influencing User Engagement in Social Media Posts on Lifestyle Risk Factors: Systematic Review.Journal of medical Internet research · 2024Pooled it
- A Model of Social Media Effects in Public Health Communication Campaigns: Systematic Review.Journal of medical Internet research · 2023Pooled it
- Engagement With and Acceptability of Digital Media Platforms for Use in Improving Health Behaviors Among Vulnerable Families: Systematic Review.Journal of medical Internet research · 2023Pooled it
- Evaluating the Effectiveness of Internet-Based Communication for Public Health: Systematic Review.Journal of medical Internet research · 2022Pooled it
- When Risk Is Invisible: Alcohol-Cancer Communication on Social Media.Alcohol, clinical & experimental research · 2026Article
- Novel Internet Advertising Approach to Raise Public Awareness About Metabolic Dysfunction-Associated Steatotic Liver Disease.JGH open : an open access journal of gastroenterology and hepatology · 2026Article
- Swap Up: Four Years of Behavior Change Through Adolescent Nutrition Education in Oklahoma.Health education & behavior : the official publication of the Society for Public Health Education · 2026Article
- Perceptions of User-Generated Content as a Source of Health Messages in Smoking Cessation Mobile Interventions: Focus Group Study.JMIR human factors · 2025Article
- Using Instagram to Promote Youth Mental Health: Feasibility and Acceptability of a Brief Social Contact-Based Video Intervention to Reduce Depression Stigma.JAACAP open · 2025Article
- 1-866-QUIT-YES: How the Illinois Tobacco Quitline Reached Priority Audiences Through Mass Media.Tobacco use insights · 2025Article
- Association between health-related social media use and E-cigarette use among current cigarette users: the roles of anti-tobacco messages and harm perception.BMC public health · 2024Article
- Characterizing Anti-Vaping Posts for Effective Communication on Instagram Using Multimodal Deep Learning.Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco · 2024Article
- Mapping automatic social media information disorder. The role of bots and AI in spreading misleading information in society.PloS one · 2024Article
- Associations between Health-Related Use of Social Media and Positive Lifestyle Behaviors: Findings from a Representative Sample of US Adult Smokers.Substance use & misuse · 2024Article
- A model of purchase intention of complementary and alternative medicines: the role of social media influencers' endorsements.BMC complementary medicine and therapies · 2023Article
- Exploring motivations for engagement with the Healthy Lunch Box campaign on social media.Health promotion international · 2023Article
- Social Media Data Mining of Antitobacco Campaign Messages: Machine Learning Analysis of Facebook Posts.Journal of medical Internet research · 2023Article
- Non-monetary narratives motivate businesses to engage with climate change.Sustainability science · 2023Article
- Tobacco and cannabis use advertisements targeting adolescents and young adults on Snapchat in 2019.Preventive medicine reports · 2022Article
- Age Differences in Preferred Methods of Obtaining and Understanding Health Related Information During the COVID-19 Pandemic in Australia.Frontiers in public health · 2022Article
Corrections and comments
- Erratum issued
Authors and funding
8 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundPublic health organizations have begun to use social media to increase awareness of health harm and positively improve health behavior. Little is known about effective strategies to disseminate health education messages digitally and ultimately achieve optimal audience engagement.
objectiveThis study aims to assess the difference in audience engagement with identical antismoking health messages on three social media sites (Twitter, Facebook, and Instagram) and with a referring link to a tobacco prevention website cited in these messages. We hypothesized that health messages might not receive the same user engagement on these media, although these messages were identical and distributed at the same time.
methodsWe measured the effect of health promotion messages on the risk of smoking among users of three social media sites (Twitter, Facebook, and Instagram) and disseminated 1275 health messages between April 19 and July 12, 2017 (85 days). The identical messages were distributed at the same time and as organic (unpaid) and advertised (paid) messages, each including a link to an educational website with more information about the topic. Outcome measures included message engagement (ie, the click-through rate [CTR] of the social media messages) and educational website engagement (ie, the CTR on the educational website [wCTR]). To analyze the data and model relationships, we used mixed effects negative binomial regression, z-statistic, and the Hosmer-Lemeshow goodness-of-fit test.
resultsComparisons between social media sites showed that CTRs for identical antitobacco health messages differed significantly across social media (P<.001 for all). Instagram showed the statistically significant highest overall mean message engagement (CTR=0.0037; 95% CI 0.0032-0.0042), followed by Facebook (CTR=0.0026; 95% CI 0.0022-0.0030) and Twitter (CTR=0.0015; 95% CI 0.0013-0.0017). Facebook showed the highest as well as the lowest CTR for any individual message. However, the message CTR is not indicative of user engagement with the educational website content. Pairwise comparisons of the social media sites differed with respect to the wCTR (P<.001 for all). Messages on Twitter showed the lowest CTR, but they resulted in the highest level of website engagement (wCTR=0.6308; 95% CI 0.5640-0.6975), followed by Facebook (wCTR=0.2213; 95% CI 0.1932-0.2495) and Instagram (wCTR=0.0334; 95% CI 0.0230-0.0438). We found a statistically significant higher CTR for organic (unpaid) messages (CTR=0.0074; 95% CI 0.0047-0.0100) compared with paid advertisements (CTR=0.0022; 95% CI 0.0017-0.0027; P<.001 and P<.001, respectively).
conclusionsOur study provides evidence-based insights to guide the design of health promotion efforts on social media. Future studies should examine the platform-specific impact of psycholinguistic message variations on user engagement, include newer sites such as Snapchat and TikTok, and study the correlation between web-based behavior and real-world health behavior change. The need is urgent in light of increased health-related marketing and misinformation on social media.
Indexed as
Identifiers
What OpenQuestion holds
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.