Evidence map›Paper›PMID 38164328›Full record

ArticleBulletin of the World Health Organization2024

Spatiotemporal trends in COVID-19 vaccine sentiments on a social media platform and correlations with reported vaccine coverage.

Xinyu Zhou, Xu Zhang, Heidi J Larson, Alexandre de Figueiredo, Mark Jit, Samah Fodeh, Sten H Vermund, Shujie Zang, Leesa Lin, Zhiyuan Hou

Open access · diamondAbstract read
In one paragraph

Article in Bulletin of the World Health Organization, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
4.2field-weighted citation impact, top 5% of its field
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

7 citing papers in PubMed, 9 citations in OpenAlex.

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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

10 authors at 2 institutions in 2 countries.

Xinyu ZhouSchool of Public Health, NHC Key Laboratory of Health Technology Assessment, and Global Health Institute, Fudan University, 130 Dong'an Road, Shanghai, 200032, China.
Xu ZhangSchool of Public Health, NHC Key Laboratory of Health Technology Assessment, and Global Health Institute, Fudan University, 130 Dong'an Road, Shanghai, 200032, China.
Heidi J LarsonDepartment of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, England.
Alexandre de FigueiredoDepartment of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, England.
Mark JitDepartment of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, England.
Samah FodehDepartment of Emergency Medicine, Yale School of Medicine, New Haven, United States of America (USA).
Sten H VermundDepartment of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, USA.
Shujie ZangSchool of Public Health, NHC Key Laboratory of Health Technology Assessment, and Global Health Institute, Fudan University, 130 Dong'an Road, Shanghai, 200032, China.
Leesa LinDepartment of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, England.
Zhiyuan HouSchool of Public Health, NHC Key Laboratory of Health Technology Assessment, and Global Health Institute, Fudan University, 130 Dong'an Road, Shanghai, 200032, China.
London School of Hygiene & Tropical Medicine · GBYale University · US

Funding

Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
NCATS NIH HHS UL1 TR001863
6 · The paper itself

Abstract

Objective: To assess spatiotemporal trends in, and determinants of, the acceptance of coronavirus disease 2019 (COVID-19) vaccination globally, as expressed on the social media platform X (formerly Twitter). Methods: We collected over 13 million posts on the platform regarding COVID-19 vaccination made between November 2020 and March 2022 in 90 languages. Multilingual deep learning XLM-RoBERTa models annotated all posts using an annotation framework after being fine-tuned on 8125 manually annotated, English-language posts. The annotation results were used to assess spatiotemporal trends in COVID-19 vaccine acceptance and confidence as expressed by platform users in 135 countries and territories. We identified associations between spatiotemporal trends in vaccine acceptance and country-level characteristics and public policies by using univariate and multivariate regression analysis. Findings: A greater proportion of platform users in the World Health Organization's South-East Asia, Eastern Mediterranean and Western Pacific Regions expressed vaccine acceptance than users in the rest of the world. Countries in which a greater proportion of platform users expressed vaccine acceptance had higher COVID-19 vaccine coverage rates. Trust in government was also associated with greater vaccine acceptance. Internationally, vaccine acceptance and confidence declined among platform users as: (i) vaccination eligibility was extended to adolescents; (ii) vaccine supplies became sufficient; (iii) nonpharmaceutical interventions were relaxed; and (iv) global reports on adverse events following vaccination appeared. Conclusion: Social media listening could provide an effective and expeditious means of informing public health policies during pandemics, and could supplement existing public health surveillance approaches in addressing global health issues.

Indexed as

COVID-19Social MediaAdolescentAttitudeCOVID-19 VaccinesHumansVaccinationCOVID-19 Vaccines

Identifiers

PMID38164328
PMCPMC10753281
OpenAlexW4390507156

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.