Evidence map›Paper›PMID 36348980›Full record

ArticleJMIR infodemiology

Negative COVID-19 Vaccine Information on Twitter: Content Analysis.

Niko Yiannakoulias, J Connor Darlington, Catherine E Slavik, Grant Benjamin

Open access · goldAbstract read
In one paragraph

Article in JMIR infodemiology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
1.9field-weighted citation impact, top 14% of its field
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

6 citing papers in PubMed, 1 synthesis or guideline pooled it, 12 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
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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

4 authors at 4 institutions in 2 countries.

Niko YiannakouliasSchool of Earth, Environment and Society McMaster University Hamilton, ON Canada.ORCID https://orcid.org/0000-0003-3443-1071
J Connor DarlingtonSchool of Geography and Environmental Management University of Waterloo Waterloo, ON Canada.ORCID https://orcid.org/0000-0002-4232-5714
Catherine E SlavikCenter for Science Communication Research School of Journalism and Communication University of Oregon Eugene, OR United States.ORCID https://orcid.org/0000-0002-3077-239X
Grant BenjaminDepartment of Economics University of Toronto Toronto, ON Canada.ORCID https://orcid.org/0000-0003-0744-9428
McMaster University · CAUniversity of Oregon · USUniversity of Toronto · CAUniversity of Waterloo · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Social media platforms, such as Facebook, Instagram, Twitter, and YouTube, have a role in spreading anti-vaccine opinion and misinformation. Vaccines have been an important component of managing the COVID-19 pandemic, so content that discourages vaccination is generally seen as a concern to public health. However, not all negative information about vaccines is explicitly anti-vaccine, and some of it may be an important part of open communication between public health experts and the community. Objective: This research aimed to determine the frequency of negative COVID-19 vaccine information on Twitter in the first 4 months of 2021. Methods: We manually coded 7306 tweets sampled from a large sampling frame of tweets related to COVID-19 and vaccination collected in early 2021. We also coded the geographic location and mentions of specific vaccine producers. We compared the prevalence of anti-vaccine and negative vaccine information over time by author type, geography (United States, United Kingdom, and Canada), and vaccine developer. Results: We found that 1.8% (131/7306) of tweets were anti-vaccine, but 21% (1533/7306) contained negative vaccine information. The media and government were common sources of negative vaccine information but not anti-vaccine content. Twitter users from the United States generated the plurality of negative vaccine information; however, Twitter users in the United Kingdom were more likely to generate negative vaccine information. Negative vaccine information related to the Oxford/AstraZeneca vaccine was the most common, particularly in March and April 2021. Conclusions: Overall, the volume of explicit anti-vaccine content on Twitter was small, but negative vaccine information was relatively common and authored by a breadth of Twitter users (including government, medical, and media sources). Negative vaccine information should be distinguished from anti-vaccine content, and its presence on social media could be promoted as evidence of an effective communication system that is honest about the potential negative effects of vaccines while promoting the overall health benefits. However, this content could still contribute to vaccine hesitancy if it is not properly contextualized.

Indexed as

content analysisCOVID-19health communicationinfodemiologymisinformationsentiment analysissocial mediaTwittervaccine acceptancevaccine hesitancyvaccine misinformationweb-based health information

Identifiers

PMID36348980
PMCPMC9632001
OpenAlexW4293070677

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.