{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T01:06:32Z","timestamp":1791939992589,"version":"4.3.4"},"reference-count":73,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J Med Internet Res"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec sec-type=\"background\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>YouTube is the primary global video platform, hosting both authoritative health information and vaccine-skeptic viewpoints. However, engagement dynamics remain poorly understood.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"objective\">\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"methods\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"results\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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\u00d7 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"conclusions\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Our analysis suggests that the vaccine-hesitant content can dominate YouTube\u2019s engagement ecosystem through rapid early-stage amplification, which has direct implications for public health intervention timing and platform governance policy.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.2196\/88519","type":"journal-article","created":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T14:55:08Z","timestamp":1777560908000},"page":"e88519-e88519","source":"Crossref","is-referenced-by-count":1,"title":["Engagement Trends in Online Vaccine Content: Longitudinal YouTube Study"],"prefix":"10.2196","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3068-5111","authenticated-orcid":false,"given":"Michele","family":"Tizzani","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5560-4109","authenticated-orcid":false,"given":"Yelena","family":"Mejova","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2026,6,19]]},"reference":[{"issue":"16","key":"R1","doi-asserted-by":"publisher","first-page":"4031","DOI":"10.1073\/pnas.1704507114","article-title":"Simply put: vaccination saves lives","volume":"114","author":"Orenstein","journal-title":"Proc Natl Acad Sci USA"},{"key":"R2","doi-asserted-by":"publisher","DOI":"10.1016\/j.healthpol.2025.105361","article-title":"Policy recommendations for healthcare authorities, organizations and professionals on vaccine hesitancy in Europe: from evidence to practice","volume":"158","author":"Correia","journal-title":"Health Policy"},{"key":"R3","unstructured":"Global partners announce a new effort \u2013 \u201cThe Big Catch-Up\u201d \u2013 to vaccinate millions of children and restore immunization progress lost during the pandemic. 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