Evidence map›Paper›PMID 40354114›Full record

ArticleJournal of medical Internet research2025

Types of HPV Vaccine Misinformation Circulating on Twitter (X) That Parents Find Most Concerning: Insights From a Cross-Sectional Survey and Content Analysis.

Jennifer C Morgan, Sarah Badlis, Katharine J Head, Gregory Zimet, Joseph N Cappella, Melanie L Kornides

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
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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

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Jennifer C MorganDepartment of Family and Community Health, School of Nursing, University of Pennsylvania, Philadelphia, PA, United States.ORCID 0000-0002-9304-0284
Sarah BadlisDepartment of Family and Community Health, School of Nursing, University of Pennsylvania, Philadelphia, PA, United States.ORCID 0009-0001-4285-9505
Katharine J HeadCommunication Studies, School of Liberal Arts, Indiana University, Indianapolis, IN, United States.ORCID 0000-0001-8946-1716
Gregory ZimetDepartment of Pediatrics, School of Medicine, Indiana University, Indianapolis, IN, United States.ORCID 0000-0003-3835-937X
Joseph N CappellaAnnenberg School for Communication, University of Pennsylvania, Philadelphia, PA, United States.ORCID 0000-0002-1998-4826
Melanie L KornidesDepartment of Family and Community Health, School of Nursing, University of Pennsylvania, Philadelphia, PA, United States.ORCID 0000-0003-1900-4530

Funding

An inoculation theory-based messaging intervention addressing misinformation about HPV vaccine on social media: The Inoculate for HPV Vaccine randomized controlled trialR37CA259210 · NCI · UNIVERSITY OF PENNSYLVANIA · PI KORNIDES, MELANIE LYNN · 2022 to 2025
$1.9M
NCI NIH HHS R37 CA259210
6 · The paper itself

Abstract

backgroundParents frequently use social media as a source of information about the human papillomavirus (HPV) vaccine. Our previous work identified that, on Twitter (now X), almost 25% of tweets about the HPV vaccine contain misinformation, and these tweets receive higher audience engagement than accurate tweets. Exposure to misinformation can increase vaccine hesitancy, but the types of misinformation found on social media vary widely, and not all misinformation exposure influences vaccine attitudes and vaccine uptake. Despite the prevalence of misinformation and antivaccine information on social media, little work has assessed parents' assessments of these posts.

objectiveThis study examines which types of misinformation on Twitter parents find the most concerning.

methodsIn April 2022, we surveyed 263 US parents of children ages 7-10 years using a Qualtrics survey panel. They viewed a first round of 9 randomly selected tweets from a pool of 126 tweets circulating on Twitter that contained misinformation about the HPV vaccine. Then parents selected up to 3 that they found most concerning. The process was repeated once more with 9 selected from the pool of 117 messages not shown in the first round. Using this information, a concern score for each tweet was calculated based on the number of parents who viewed the tweet and selected it as concerning. In total, 2 researchers independently coded the misinformation tweets to identify rhetorical strategies used and health concerns mentioned. Multiple linear regression tested whether tweet content significantly predicted the concern score of the tweet.

resultsParental concern about the different misinformation tweets varied widely, with some misinformation being selected as most concerning just 2.8% of the time it was viewed and other misinformation being selected 79.5% of the time it was viewed. Multiple beta regression analyses found that misinformation tweets using negative emotional appeals (b=.79, P<.001), expressing pharmaceutical company skepticism (b=.36, P=.036), invoking governmental authority (b=.44, P=.02), and mentioning hospitalization (b=1.00, P=.003), paralysis (b=.54, P=.02), and infertility (b=.52, P=.04) significantly increased the percent of parents rating the misinformation tweets as most concerning.

conclusionsMisinformation about HPV vaccination is ubiquitous on social media, and it would be impossible to target and correct all of it. Counter-messaging campaigns and interventions to combat misinformation need to focus on the types of misinformation that concern parents and ultimately may impact vaccine uptake. Results from this study identify the misinformation content that parents find most concerning and provide a useful list of targets for researchers developing interventions to combat misinformation with the goal of increasing HPV vaccine uptake.

Indexed as

CommunicationPapillomavirus VaccinesParentsSocial MediaAdultChildCross-Sectional StudiesFemaleHealth Knowledge, Attitudes, PracticeHumansMaleSurveys and QuestionnairesVaccination HesitancyPapillomavirus Vaccinescontent analysiscross-sectional surveyengagementHPV vaccinemisinformationparental concernsocial mediaTweetsTwittervaccine misinformationvaccine uptake

Identifiers

PMID40354114
PMCPMC12107203

What OpenQuestion holds

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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.