Evidence map›Paper›PMID 36649235›Full record

ArticleJournal of medical Internet research2023

Applications of Social Media and Digital Technologies in COVID-19 Vaccination: Scoping Review.

Shujie Zang, Xu Zhang, Yuting Xing, Jiaxian Chen, Leesa Lin, Zhiyuan Hou

Abstract readScoping Review
In one paragraph

Article in Journal of medical Internet research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing 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

16 citing papers in PubMed.

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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

6 authors.

Shujie Zang *School of Public Health, Fudan University, Shanghai, China.ORCID 0000-0003-1972-0174
Xu Zhang *School of Public Health, Fudan University, Shanghai, China.ORCID 0000-0003-1109-6880
Yuting XingSchool of Public Health, Fudan University, Shanghai, China.ORCID 0000-0003-2307-0194
Jiaxian ChenSchool of Public Health, Fudan University, Shanghai, China.ORCID 0000-0002-4565-3772
Leesa LinDepartment of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom.ORCID 0000-0003-4123-4762
Zhiyuan HouSchool of Public Health, Fudan University, Shanghai, China.ORCID 0000-0003-3413-0076

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSocial media and digital technologies have played essential roles in disseminating information and promoting vaccination during the COVID-19 pandemic. There is a need to summarize the applications and analytical techniques of social media and digital technologies in monitoring vaccine attitudes and administering COVID-19 vaccines.

objectiveWe aimed to synthesize the global evidence on the applications of social media and digital technologies in COVID-19 vaccination and to explore their avenues to promote COVID-19 vaccination.

methodsWe searched 6 databases (PubMed, Scopus, Web of Science, Embase, EBSCO, and IEEE Xplore) for English-language articles from December 2019 to August 2022. The search terms covered keywords relating to social media, digital technology, and COVID-19 vaccines. Articles were included if they provided original descriptions of applications of social media or digital health technologies/solutions in COVID-19 vaccination. Conference abstracts, editorials, letters, commentaries, correspondence articles, study protocols, and reviews were excluded. A modified version of the Appraisal Tool for Cross-Sectional Studies (AXIS tool) was used to evaluate the quality of social media-related studies. The review was undertaken with the guidance of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews.

resultsA total of 178 articles were included in our review, including 114 social media articles and 64 digital technology articles. Social media has been applied for sentiment/emotion analysis, topic analysis, behavioral analysis, dissemination and engagement analysis, and information quality analysis around COVID-19 vaccination. Of these, sentiment analysis and topic analysis were the most common, with social media data being primarily analyzed by lexicon-based and machine learning techniques. The accuracy and reliability of information on social media can seriously affect public attitudes toward COVID-19 vaccines, and misinformation often leads to vaccine hesitancy. Digital technologies have been applied to determine the COVID-19 vaccination strategy, predict the vaccination process, optimize vaccine distribution and delivery, provide safe and transparent vaccination certificates, and perform postvaccination surveillance. The applied digital technologies included algorithms, blockchain, mobile health, the Internet of Things, and other technologies, although with some barriers to their popularization.

conclusionsThe applications of social media and digital technologies in addressing COVID-19 vaccination-related issues represent an irreversible trend. Attention should be paid to the ethical issues and health inequities arising from the digital divide while applying and promoting these technologies.

Indexed as

COVID-19Social MediaCOVID-19 VaccinesCross-Sectional StudiesDigital TechnologyHumansPandemicsReproducibility of ResultsVaccinationCOVID-19 VaccinesCOVID-19digital healthreviewsocial mediavaccination

Identifiers

PMID36649235
PMCPMC9924059

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.