Evidence map›Paper›PMID 35061560›Full record

ArticleHuman vaccines & immunotherapeutics2022

Cross-platform spread: vaccine-related content, sources, and conspiracy theories in YouTube videos shared in early Twitter COVID-19 conversations.

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

Open access · goldAbstract read
In one paragraph

Article in Human vaccines & immunotherapeutics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed, 1 pooled it
25.2field-weighted citation impact, top 1% 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

24 citing papers in PubMed, 1 synthesis or guideline pooled it, 65 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. Article
  8. YouTube and COVID-19 vaccines: A mini scoping review.Human vaccines & immunotherapeutics · 2023
    Article
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  10. Article
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  14. 'Vaccine as aFrontiers in psychology · 2023
    Article
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  18. Article
  19. 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
  20. 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

5 authors at 4 institutions in 2 countries.

Tamar GinossarDepartment of Communication and Journalism, Institute for Social Research, The University of New Mexico, Albuquerque, NM, USA.
Iain J CruickshankInstitute for Software Research, Carnegie Mellon University, Pittsburgh, PA, USA.
Elena ZhelevaComputer Science Department, University of Illinois at Chicago, Chicago, IL, USA.
Jason SulskisComputer Science Department, University of Illinois at Chicago, Chicago, IL, USA.
Tanya Berger-WolfTranslational Data Analytics Institute, Computer Science Engineering, Electrical, Computer Engineering, and Evolution, Ecology, and Organismal Biology, Ohio State University, Columbus, OH, USA.
University of Illinois Chicago · USCarnegie Mellon University · USInstitute for Social Research · NOThe Ohio State University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

High uptake of vaccinations is essential in fighting infectious diseases, including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that causes the ongoing coronavirus disease 2019 (COVID-19) pandemic. Social media play a crucial role in propagating misinformation about vaccination, including through conspiracy theories and can negatively impact trust in vaccination. Users typically engage with multiple social media platforms; however, little is known about the role and content of cross-platform use in spreading vaccination-related information. This study examined the content and dynamics of YouTube videos shared in vaccine-related tweets posted to COVID-19 conversations before the COVID-19 vaccine rollout. We screened approximately 144 million tweets posted to COVID-19 conversations and identified 930,539 unique tweets in English that discussed vaccinations posted between 1 February and 23 June 2020. We then identified links to 2,097 unique YouTube videos that were tweeted. Analysis of the video transcripts using Latent Dirichlet Allocation topic modeling and independent coders indicate the dominance of conspiracy theories. Following the World Health Organization's declaration of the COVID-19 outbreak as a public health emergency of international concern, anti-vaccination frames rapidly transitioned from claiming that vaccines cause autism to pandemic conspiracy theories, often featuring Bill Gates. Content analysis of the 20 most tweeted videos revealed that the majority (n = 15) opposed vaccination and included conspiracy theories. Their spread on Twitter was consistent with spamming and coordinated efforts. These findings show the role of cross-platform sharing of YouTube videos over Twitter as a strategy to propagate primarily anti-vaccination messages. Future policies and interventions should consider how to counteract misinformation spread via such cross-platform activities.

Indexed as

COVID-19Social MediaVaccinesCommunicationCOVID-19 VaccinesHumansSARS-CoV-2COVID-19 VaccinesVaccinesAnti-vaccinationbig dataconspiracy theoriescross-platformdata mining social mediamachine learningmisinformationTwittervaccinationsvaccine hesitancyYouTube

Identifiers

PMID35061560
PMCPMC8920146
OpenAlexW4206957470

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.