Evidence map›Paper›PMID 38328781›Full record

ArticlePNAS nexus2024

Perceived experts are prevalent and influential within an antivaccine community on Twitter.

Mallory J Harris, Ryan Murtfeldt, Shufan Wang, Erin A Mordecai, Jevin D West

Abstract read
In one paragraph

Article in PNAS nexus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 pooled it
–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

8 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. "They Only Silence the Truth": COVID-19 retractions and the politicization of science.Public understanding of science (Bristol, England) · 2025
    Article
  6. Article
  7. Observational
  8. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Mallory J HarrisDepartment of Biology, Stanford University, Stanford, CA 94305, USA.ORCID https://orcid.org/0000-0001-8196-0430
Ryan MurtfeldtInformation School, University of Washington, Seattle, WA 98105, USA.ORCID https://orcid.org/0009-0004-4586-0597
Shufan WangInformation School, University of Washington, Seattle, WA 98105, USA.ORCID https://orcid.org/0009-0007-4212-0878
Erin A MordecaiDepartment of Biology, Stanford University, Stanford, CA 94305, USA.ORCID https://orcid.org/0000-0002-4402-5547
Jevin D WestDepartment of Biology, Stanford University, Stanford, CA 94305, USA.ORCID https://orcid.org/0000-0002-4118-0322

Funding

The child health impacts of overlapping vector-borne diseases in KenyaR01AI102918 · NIAID · STANFORD UNIVERSITY · PI Angelle Desiree LaBeaud · 2013 to 2026
$7.7M
Leveraging environmental drivers to predict vector-borne disease transmissionR35GM133439 · NIGMS · STANFORD UNIVERSITY · PI Erin Mordecai · 2019 to 2026
$3.2M
Modeling the influence of temperature on the evolution of vector-virus interactionsR01AI168097 · NIAID · WADSWORTH CENTER · PI Alexander Timothy Ciota · 2022 to 2026
$2.4M
NIAID NIH HHS R01 AI102918NIAID NIH HHS R01 AI168097NIGMS NIH HHS R35 GM133439
6 · The paper itself

Abstract

Perceived experts (i.e. medical professionals and biomedical scientists) are trusted sources of medical information who are especially effective at encouraging vaccine uptake. The role of perceived experts acting as potential antivaccine influencers has not been characterized systematically. We describe the prevalence and importance of antivaccine perceived experts by constructing a coengagement network of 7,720 accounts based on a Twitter data set containing over 4.2 million posts from April 2021. The coengagement network primarily broke into two large communities that differed in their stance toward COVID-19 vaccines, and misinformation was predominantly shared by the antivaccine community. Perceived experts had a sizable presence across the coengagement network, including within the antivaccine community where they were 9.8% of individual, English-language users. Perceived experts within the antivaccine community shared low-quality (misinformation) sources at similar rates and academic sources at higher rates compared to perceived nonexperts in that community. Perceived experts occupied important network positions as central antivaccine users and bridges between the antivaccine and provaccine communities. Using propensity score matching, we found that perceived expertise brought an influence boost, as perceived experts were significantly more likely to receive likes and retweets in both the antivaccine and provaccine communities. There was no significant difference in the magnitude of the influence boost for perceived experts between the two communities. Social media platforms, scientific communications, and biomedical organizations may focus on more systemic interventions to reduce the impact of perceived experts in spreading antivaccine misinformation.

Indexed as

antivaccinemisinformationpublic healthsocial media

Identifiers

PMID38328781
PMCPMC10847722

What OpenQuestion holds

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LicenceCC BY
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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.