Evidence map›Paper›PMID 40809071›Full record

ArticleInformation, communication and society2025

Who is perceived to be an expert on COVID-19 vaccines on social media?: Biomedical credentials confer expertise, even among vaccine-hesitant and Conservative observers.

Madeline Jalbert, Mallory Harris, Luke Williams

Abstract read
In one paragraph

Article in Information, communication and society, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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.

Madeline JalbertCenter for an Informed Public, University of Washington, Seattle, United States.
Mallory HarrisCenter for an Informed Public, University of Washington, Seattle, United States.
Luke WilliamsCenter for an Informed Public, University of Washington, Seattle, United States.

Funding

Leveraging environmental drivers to predict vector-borne disease transmissionR35GM133439 · NIGMS · STANFORD UNIVERSITY · PI Erin Mordecai · 2019 to 2026
$3.2M
NIGMS NIH HHS R35 GM133439
6 · The paper itself

Abstract

Who is perceived to be an expert on COVID-19 vaccination on social media? We conducted four experimental studies investigating how the presence of biomedical credentials in social media profiles impacts users' perceived expertise. Participants viewed a series of Twitter profiles that appeared with or without biomedical credentials and judged to what extent they believed each user was an expert on the topic of COVID-19 vaccination. We found that the presence of biomedical credentials consistently increased perceptions of expertise, including among unvaccinated, vaccine-hesitant, and conservative participants. In some cases, participants who were less vaccine-hesitant, had been vaccinated, and identified as more liberal were generally more influenced by the presence of credentials when judging COVID-19 vaccination expertise; however, credentials still had a significant and large effect regardless of vaccination status and attitude or political partisanship, and was much larger than the effect of any moderators. These findings support existing observations that biomedical credentials may be leveraged by both pro- and anti-vaccine communities to increase perceived credibility and message reach, and counter the narrative that conservatives and those with anti-vaccination attitudes do not recognize biomedical credentials as conferring expertise.

Indexed as

anti-vaccineCOVID-19 vaccinationexpertisejudgment and decision-makingmisinformationsocial media

Identifiers

PMID40809071
PMCPMC12341664

What OpenQuestion holds

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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.