Evidence map›Paper›PMID 35270408›Full record

ArticleInternational journal of environmental research and public health2022

How News Agencies' Twitter Posts on COVID-19 Vaccines Attract Audiences' Twitter Engagement: A Content Analysis.

Di Wang, Jiahui Lu

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 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

2 authors.

Di WangFaculty of Humanities and Arts, Macau University of Science and Technology, R322, Avenida Wai Long, Taipa, Macao 999078, China.ORCID 0000-0003-2736-8369
Jiahui LuSchool of New Media and Communication, Tianjin University, No. 92, Weijin Road, Nankai District, Tianjin 300072, China.ORCID 0000-0003-2984-7435

Funding

The Higher Education Fund of the Macao S.A.R. Government HSS-MUST-2021-01
6 · The paper itself

Abstract

As the most important global news distributors, the big three international news agencies' reports about COVID-19 vaccines have a great influence on people's understanding of them. Based on the health belief model (HBM), we examined which constructs in the HBM were related to audiences' Twitter engagement and the differences among the agencies. We content-analyzed 1162 COVID-19 vaccine-related tweets from three international news agencies' Twitter accounts (@AFPespanol, @AP, @Reuters) from 2 December 2020 to 31 January 2021. The results showed that the most-used HBM construct was barriers, followed by benefits, susceptibility, cues to action, severity, and self-efficacy. About half of the tweets used a positive tone and nearly half of the tweets used a neutral tone, while only 3.1% of the tweets used a negative tone. Reuters used a significantly more negative tone, more neutral tone, and less positive tone than was expected. AFP used a significantly more positive tone and less neutral tone than was expected. The effectiveness of utilizing HBM constructs for vaccination promotion strongly depends on the audience context. The use of HBM constructs for vaccination was generally effective for Reuters but seems to have backfired for AFP.

Indexed as

COVID-19Social MediaCOVID-19 VaccinesHumansSARS-CoV-2VaccinationCOVID-19 Vaccinescontent analysisCOVID-19COVID-19 vaccinehealth belief model (HBM)Twitter

Identifiers

PMID35270408
PMCPMC8910090

What OpenQuestion holds

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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.