Evidence map›Paper›PMID 39773610›Full record

ArticleBMC public health2025

A thematic analysis of UK COVID-19 vaccine hesitancy discussions on Twitter.

Reeshma Jameel, Sheila Greenfield, Anna Lavis

Abstract read
In one paragraph

Article in BMC public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
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

3 authors.

Reeshma JameelCollege of Medicine and Health, University of Birmingham, Birmingham, UK. reeshmajameel@yahoo.co.uk.
Sheila GreenfieldDepartment of Applied Health Sciences, University of Birmingham, Birmingham, UK.
Anna LavisDepartment of Applied Health Sciences and Institute for Mental Health, University of Birmingham, Birmingham, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundFollowing UK approval of the Pfizer-BioNTech and Oxford-AstraZeneca vaccines on 2/12/20 and 30/12/20 respectively, discussions about them emerged on the social media platform Twitter, (now 'X'). Previous research has shown that Twitter/ X is used by the UK public to engage with public health announcements and that social media influences public opinions of vaccines, including COVID-19 vaccines, globally. This study explored discussions on Twitter posted in response to the UK government's posts introducing the Pfizer-BioNTech and Oxford-AstraZeneca vaccines. The aim was to investigate vaccine hesitant views, and thereby identify barriers and facilitators to COVID-19 vaccine uptake in the UK.

methodsOnline ethnography was used to collect responses ('tweet replies') to 14 Twitter posts published by officials or departments of the UK government on the dates the Pfizer-BioNTech and Oxford-AstraZeneca vaccines received approval from the Medicines and Healthcare products Regulatory Agency (2/12/20 and 30/12/20, respectively). 16,508 responses were collected and those expressing vaccine hesitancy were analysed using reflexive thematic analysis.

resultsThree themes that underpinned Twitter posters' vaccine hesitancy were identified: (1) Concerns about vaccine development and safety, (2) Information, misinformation and disinformation, (3) Distrust: Politics and 'Big Pharma'. From these themes, eight barriers and eight facilitators to UK COVID-19 vaccine uptake were identified.

conclusionThis paper highlights key obstacles to COVID-19 vaccine acceptance in the UK based on views from Twitter and contributes to the emerging literature on the relationship between social media and the public response to COVID-19 vaccines. In so doing, this analysis offers insights that are useful for the development of vaccine communication strategies more broadly, both in and beyond future pandemics, to ensure that public concerns are addressed, and misinformation and disinformation are appropriately countered.

Indexed as

COVID-19COVID-19 VaccinesSocial MediaVaccination HesitancyHumansPublic OpinionSARS-CoV-2United KingdomCOVID-19 VaccinesCOVID-19 vaccinesOnline ethnographyQualitative researchSocial mediaThematic analysisTwitterUKVaccine hesitancy

Identifiers

PMID39773610
PMCPMC11705654

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.