Evidence map›Paper›PMID 36844368›Full record

ArticleJAMIA open2023

#Coronavirus on TikTok: user engagement with misinformation as a potential threat to public health behavior.

Jonathan D Baghdadi, K C Coffey, Rachael Belcher, James Frisbie, Naeemul Hassan, Danielle Sim, Rena D Malik

Abstract read
In one paragraph

Article in JAMIA open, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 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

7 authors.

Jonathan D BaghdadiDivision of Genomic Epidemiology and Clinical Outcomes, Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, Maryland, USA.ORCID https://orcid.org/0000-0002-2442-0654
K C CoffeyDivision of Genomic Epidemiology and Clinical Outcomes, Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, Maryland, USA.
Rachael BelcherDivision of Urology, Department of Surgery, University of Maryland School of Medicine, Baltimore, Maryland, USA.
James FrisbieDivision of Urology, Department of Surgery, University of Maryland School of Medicine, Baltimore, Maryland, USA.
Naeemul HassanCollege of Information Studies, University of Maryland, College Park, Maryland, USA.
Danielle SimDivision of Urology, Department of Surgery, University of Maryland School of Medicine, Baltimore, Maryland, USA.
Rena D MalikDivision of Urology, Department of Surgery, University of Maryland School of Medicine, Baltimore, Maryland, USA.

Funding

Optimizing Implementation of Multiplex Molecular Panel Testing to Reduce Diagnostic ErrorK08HS028854 · AHRQ · UNIVERSITY OF MARYLAND BALTIMORE · PI BAGHDADI, JONATHAN · 2022 to 2025
$594k
AHRQ HHS K08 HS028854
6 · The paper itself

Abstract

Coronavirus disease (COVID)-related misinformation is prevalent online, including on social media. The purpose of this study was to explore factors associated with user engagement with COVID-related misinformation on the social media platform, TikTok. A sample of TikTok videos associated with the hashtag #coronavirus was downloaded on September 20, 2020. Misinformation was evaluated on a scale (low, medium, and high) using a codebook developed by experts in infectious diseases. Multivariable modeling was used to evaluate factors associated with number of views and presence of user comments indicating intention to change behavior. One hundred and sixty-six TikTok videos were identified and reviewed. Moderate misinformation was present in 36 (22%) videos viewed a median of 6.8 million times (interquartile range [IQR] 3.6-16 million), and high-level misinformation was present in 11 (7%) videos viewed a median of 9.4 million times (IQR 5.1-18 million). After controlling for characteristics and content, videos containing moderate misinformation were less likely to generate a user response indicating intended behavior change. By contrast, videos containing high-level misinformation were less likely to be viewed but demonstrated a nonsignificant trend towards higher engagement among viewers. COVID-related misinformation is less frequently viewed on TikTok but more likely to engage viewers. Public health authorities can combat misinformation on social media by posting informative content of their own.

Indexed as

COVID-19misinformationpublic healthTikTok

Identifiers

PMID36844368
PMCPMC9949581

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.