Evidence map›Paper›PMID 36735835›Full record

ArticleJournal of medical Internet research2023

One Year of COVID-19 Vaccine Misinformation on Twitter: Longitudinal Study.

Francesco Pierri, Matthew R DeVerna, Kai-Cheng Yang, David Axelrod, John Bryden, Filippo Menczer

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing 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

21 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

6 authors.

Francesco PierriDipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, Italy.ORCID 0000-0002-9339-7566
Matthew R DeVernaObservatory on Social Media, Indiana University, Bloomington, IN, United States.ORCID 0000-0003-3578-8339
Kai-Cheng YangObservatory on Social Media, Indiana University, Bloomington, IN, United States.ORCID 0000-0003-4627-9273
David AxelrodObservatory on Social Media, Indiana University, Bloomington, IN, United States.ORCID 0000-0002-9840-8711
John BrydenObservatory on Social Media, Indiana University, Bloomington, IN, United States.ORCID 0000-0002-5301-5367
Filippo MenczerObservatory on Social Media, Indiana University, Bloomington, IN, United States.ORCID 0000-0003-4384-2876

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundVaccinations play a critical role in mitigating the impact of COVID-19 and other diseases. Past research has linked misinformation to increased hesitancy and lower vaccination rates. Gaps remain in our knowledge about the main drivers of vaccine misinformation on social media and effective ways to intervene.

objectiveOur longitudinal study had two primary objectives: (1) to investigate the patterns of prevalence and contagion of COVID-19 vaccine misinformation on Twitter in 2021, and (2) to identify the main spreaders of vaccine misinformation. Given our initial results, we further considered the likely drivers of misinformation and its spread, providing insights for potential interventions.

methodsWe collected almost 300 million English-language tweets related to COVID-19 vaccines using a list of over 80 relevant keywords over a period of 12 months. We then extracted and labeled news articles at the source level based on third-party lists of low-credibility and mainstream news sources, and measured the prevalence of different kinds of information. We also considered suspicious YouTube videos shared on Twitter. We focused our analysis of vaccine misinformation spreaders on verified and automated Twitter accounts.

resultsOur findings showed a relatively low prevalence of low-credibility information compared to the entirety of mainstream news. However, the most popular low-credibility sources had reshare volumes comparable to those of many mainstream sources, and had larger volumes than those of authoritative sources such as the US Centers for Disease Control and Prevention and the World Health Organization. Throughout the year, we observed an increasing trend in the prevalence of low-credibility news about vaccines. We also observed a considerable amount of suspicious YouTube videos shared on Twitter. Tweets by a small group of approximately 800 "superspreaders" verified by Twitter accounted for approximately 35% of all reshares of misinformation on an average day, with the top superspreader (@RobertKennedyJr) responsible for over 13% of retweets. Finally, low-credibility news and suspicious YouTube videos were more likely to be shared by automated accounts.

conclusionsThe wide spread of misinformation around COVID-19 vaccines on Twitter during 2021 shows that there was an audience for this type of content. Our findings are also consistent with the hypothesis that superspreaders are driven by financial incentives that allow them to profit from health misinformation. Despite high-profile cases of deplatformed misinformation superspreaders, our results show that in 2021, a few individuals still played an outsized role in the spread of low-credibility vaccine content. As a result, social media moderation efforts would be better served by focusing on reducing the online visibility of repeat spreaders of harmful content, especially during public health crises.

Indexed as

COVID-19Social MediaVaccinesCommunicationCOVID-19 VaccinesHumansLongitudinal StudiesCOVID-19 VaccinesVaccinescontent analysisCOVID-19infodemiologymisinformationonline health informationsocial mediatrend analysisTwittervaccine hesitancyvaccines

Identifiers

PMID36735835
PMCPMC9970010

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Registered trials

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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.