Evidence map›Paper›PMID 34665760›Full record

ArticleJournal of medical Internet research2021

Content and Dynamics of Websites Shared Over Vaccine-Related Tweets in COVID-19 Conversations: Computational Analysis.

Iain Cruickshank, Tamar Ginossar, Jason Sulskis, Elena Zheleva, Tanya Berger-Wolf

Erratum issuedAbstract read
In one paragraph

Article in Journal of medical Internet research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. Revealing Public Opinion towards the COVID-19 Vaccine with Weibo Data in China: BertFDA-Based Model.International journal of environmental research and public health · 2022
    Article
  8. Article
  9. Article
  10. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Iain Cruickshank *Center for Computational Analysis of Social and Organizational Systems, Carnegie Mellon University, Pittsburgh, PA, United States.ORCID 0000-0002-4205-5806
Tamar Ginossar *Department of Communication and Journalism, University of New Mexico, Albuquerque, NM, United States.ORCID 0000-0002-0136-6675
Jason Sulskis *Department of Computer Science, The University of Illinois at Chicago, Chicago, IL, United States.ORCID 0000-0002-9204-7695
Elena Zheleva *Department of Computer Science, The University of Illinois at Chicago, Chicago, IL, United States.ORCID 0000-0001-7662-2568
Tanya Berger-Wolf *Department of Computer Science, The University of Illinois at Chicago, Chicago, IL, United States.ORCID 0000-0001-7610-1412

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe onset of the COVID-19 pandemic and the consequent "infodemic" increased concerns about Twitter's role in advancing antivaccination messages, even before a vaccine became available to the public. New computational methods allow for analysis of cross-platform use by tracking links to websites shared over Twitter, which, in turn, can uncover some of the content and dynamics of information sources and agenda-setting processes. Such understanding can advance theory and efforts to reduce misinformation.

objectiveInformed by agenda-setting theory, this study aimed to identify the content and temporal patterns of websites shared in vaccine-related tweets posted to COVID-19 conversations on Twitter between February and June 2020.

methodsWe used triangulation of data analysis methods. Data mining consisted of the screening of around 5 million tweets posted to COVID-19 conversations to identify tweets that related to vaccination and including links to websites shared within these tweets. We further analyzed the content the 20 most-shared external websites using a mixed methods approach.

resultsOf 841,896 vaccination-related tweets identified, 185,994 (22.1%) contained links to specific websites. A wide range of websites were shared, with the 20 most-tweeted websites constituting 14.5% (27,060/185,994) of the shared websites and typically being shared for only 2 to 3 days. Traditional media constituted the majority of these 20 websites, along with other social media and governmental sources. We identified markers of inauthentic propagation for some of these links.

conclusionsThe topic of vaccination was prevalent in tweets about COVID-19 early in the pandemic. Sharing websites was a common communication strategy, and its "bursty" pattern and inauthentic propagation strategies pose challenges for health promotion efforts. Future studies should consider cross-platform use in dissemination of health information and in counteracting misinformation.

Indexed as

COVID-19Social MediaVaccinesCommunicationHumansPandemicsSARS-CoV-2Vaccinesagenda settingantivaccinationCOVID-19cross-platformdata mining of social mediamisinformationsocial mediaTwittervaccinationsvaccine hesitancy

Identifiers

PMID34665760
PMCPMC8647974

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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.