Evidence map›Paper›PMID 42320022›Full record

ArticleJournal of medical Internet research2026

Engagement Trends in Online Vaccine Content: Longitudinal YouTube Study.

Michele Tizzani, Yelena Mejova

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Michele TizzaniSection for Cognitive Systems, Department of Applied Mathematics and Computer Science, Technical University of Denmark, Richard Petersens Plads, Kgs. Lyngby, Capital Region, 2800, Denmark, 45 45 25 30 31.ORCID http://orcid.org/0000-0003-3068-5111
Yelena MejovaInstitute for Scientific Interchange, Turin, Piedmont, Italy.ORCID http://orcid.org/0000-0001-5560-4109

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: YouTube is the primary global video platform, hosting both authoritative health information and vaccine-skeptic viewpoints. However, engagement dynamics remain poorly understood. Objective: The aim of this study was to investigate the temporal and textual dynamics of engagement of the YouTube viewership with vaccination content, and specifically content that is in favor of or against vaccination. We contextualized these dynamics in the authority signals of the posting channel and the moderation actions taken by the platform. Methods: We conducted a 6-month daily longitudinal analysis of 7213 vaccine-related YouTube videos (November 2024 to May 2025) mentioning vaccination. We used zero-shot large language model classification with manual verification to classify the video stance toward vaccination, and the stance of their comments toward the video. The engagement and disagreement dynamics were modeled using Bayesian regression. Results: Our findings show engagement asymmetry between content supporting and questioning vaccination. Vaccine-hesitant videos in our sample receive substantially higher raw engagement (median likes: 40 [IQR 3-846] to 59 [IQR 3-1319]; median comments: 10 [IQR 0-160] to 18 [IQR 0-311] per video versus 3 [IQR 1-15] and 0 [IQR 0-4], respectively, for strongly provaccine content) and moderate normalized engagement rates (true median combined rate: 0.073 [IQR 0.028-0.121] to 0.069 [IQR 0.027-0.118] interactions per view versus 0.026 [IQR 0.007-0.060] for strongly pro-vaccine videos, a 2.5-2.6× difference). Descriptively, vaccine-hesitant videos reach 90% of cumulative views faster (18 [IQR 8-38] days vs 32 [IQR 18-64] days; 44% faster), while negative binomial models that adjust for total engagement volume indicate that approximately 20% of this advantage reflects genuine temporal compression independent of engagement volume. Comment analysis indicated that the vaccine-hesitant videos in our sample foster echo chambers, while the provaccine content attracts battlegrounds. Considering the sources of vaccine-related content, provaccine content tends to originate from organizations, particularly news and health institutions, while vaccine-hesitant discourse is more likely to come from individual creators, even those self-identifying as medical doctors. Moderation, on the rare occasion when it occurs (about 2% of the videos were taken down), comes after engagement saturation, limiting its effectiveness. Conclusions: Our analysis suggests that the vaccine-hesitant content can dominate YouTube's engagement ecosystem through rapid early-stage amplification, which has direct implications for public health intervention timing and platform governance policy.

Indexed as

InternetSocial MediaVaccinationVaccinesBayes TheoremDigital MediaHumansLongitudinal StudiesVaccinesBayesian modelingecho chambersengagement dynamicslongitudinal analysismisinformationpublic health policysocial mediatemporal patternsvaccine hesitancyYouTube

Identifiers

PMID42320022
PMCPMC13282043

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.