Evidence map›Paper›PMID 37467276›Full record

ArticlePLOS global public health2023

Credibility of vaccine-related content on Twitter during COVID-19 pandemic.

Samira Yousefinaghani, Rozita Dara, Alice Wang, Melissa MacKay, Andrew Papadopoulos, Shayan Sharif

Abstract read
In one paragraph

Article in PLOS global public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

6 authors.

Samira YousefinaghaniSchool of Computer Science, University of Guelph, Guelph, Ontario, Canada.ORCID https://orcid.org/0000-0001-5805-4608
Rozita DaraSchool of Computer Science, University of Guelph, Guelph, Ontario, Canada.ORCID https://orcid.org/0000-0002-3728-0275
Alice WangDepartment of Pathobiology, University of Guelph, Guelph, Ontario, Canada.ORCID https://orcid.org/0000-0003-4273-7847
Melissa MacKayDepartment of Population Medicine, University of Guelph, Guelph, Ontario, Canada.
Andrew PapadopoulosDepartment of Population Medicine, University of Guelph, Guelph, Ontario, Canada.
Shayan SharifDepartment of Pathobiology, University of Guelph, Guelph, Ontario, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

During national COVID-19 vaccine campaigns, people continuously engaged on Twitter to receive updates on the latest public health information, and to discuss and share their experiences. During this time, the spread of misinformation was widespread, which threatened the uptake of vaccines. It is therefore critical to understand the reasons behind vaccine misinformation and strategies to mitigate it. The current research aimed to understand the content of misleading tweets and the characteristics of their corresponding accounts. We performed a machine learning approach to identify misinformation in vaccine-related tweets, and calculated the demographic, engagement metrics and bot-like activities of corresponding accounts. We found critical periods where high amounts of misinformation coincided with important vaccine announcements, such as emergency approvals of vaccines. Moreover, we found Asian countries had a lower percentage of misinformation shared compared to Europe and North America. Our results showed accounts spreading misinformation had an overall 10% greater likelihood of bot activity and 15% more astroturf bot activity than accounts spreading accurate information. Furthermore, we found that accounts spreading misinformation had five times fewer followers and three times fewer verified badges than fact-sharing accounts. The findings of this study may help authorities to develop strategies to fight COVID-19 vaccine misinformation and improve vaccine uptake.

Identifiers

PMID37467276
PMCPMC10355402

What OpenQuestion holds

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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.