Evidence map›Paper›PMID 42040099›Full record

ArticleFrontiers in public health2026

Political vs. expert: the impact of influencer type on vaccination willingness.

Jiangyi He, Jinyan Hu, Yiyang Li, Yanfei Luo

Abstract read
In one paragraph

Article in Frontiers in public health, 2026. 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

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

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

4 authors.

Jiangyi HeSchool of Journalism and Communication, Henan University, Kaifeng, China.
Jinyan HuSchool of Journalism and Communication, Henan University, Kaifeng, China.
Yiyang LiSchool of Creative Media, City University of Hong Kong, Hong Kong, Hong Kong SAR, China.
Yanfei LuoSchool of Journalism and Communication, Zhengzhou University, Zhengzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: During a pandemic, influencers played a key role in promoting vaccination. This study examines the impact of influencer types (political vs. expert) on vaccination in the context of a future pandemic caused by a novel virus. Methods: A 2 × 2 between-subjects experimental design was employed, with 287 valid adult participants recruited from China. Among all participants, 41.81% were male and 58.19% were female, with an average age of 30.20 years (SD = 7.64). Participants were exposed to simulated social media posts that manipulated influencer types (political vs. expert) and message framing (gain vs. loss). Influencers were presented with different profile pictures, nicknames and background descriptions, while follower counts remained unchanged. Message framing was manipulated by presenting vaccination information using a gain frame (emphasizing the benefits of vaccination) or a loss frame (emphasizing the risks of non-vaccination). Analysis of variance (ANOVA) was used to test the impact of influencer type, and its interaction with message framing on vaccination intentions. Meanwhile, the mediating effect of perceived credibility was tested using the PROCESS Macro Model 4 in R. Results: Results indicate a key main effect of influencer type on vaccination willingness ( Discussions: Our findings highlight the importance of political influencers during pandemics, a discovery consistent with the informational entropy-based notion of value. This persuasive effect is achieved by enhancing perceived credibility, which can be explained by meaning transfer. Political influencers often align with mainstream authorities, and audiences view their messages as the government's stance. Therefore, the public's trust in the government shifts to political influencers, thus enhancing their influence during the pandemic. Interaction analysis indicates that correctly matching influencer type and message framing enhances vaccination intention. This matching effect is driven by the image the influencer represent. These findings contribute to more effective vaccine promotion during pandemics.

Indexed as

COVID-19PoliticsVaccinationVaccination HesitancyAdultChinaFemaleHumansMaleMedia ExposurePandemicsSocial MediaYoung Adultinfluencer typemessage framingpandemicperceived credibilityvaccination

Identifiers

PMID42040099
PMCPMC13106328

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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.