Evidence map›Paper›PMID 37810004›Full record

ArticleHeliyon2023

Gender as a moderating variable in online misinformation acceptance during COVID-19.

Ahmed Mansoori, Khalaf Tahat, Dina Tahat, Mohammad Habes, Said A Salloum, Hesham Mesbah, Mokhtar Elareshi

Abstract read
In one paragraph

Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Ahmed MansooriMedia & Creative Industries Department, United Arab Emirates University, Al Ain, United Arab Emirates.
Khalaf TahatMedia & Creative Industries Department, United Arab Emirates University, Al Ain, United Arab Emirates.
Dina TahatEducation & Humanities Sciences College, Al Ain University, Al Ain, United Arab Emirates.
Mohammad HabesRadio & TV Department, Yarmouk University, Irbid, Jordan.
Said A SalloumMedia & Creative Industries Department, United Arab Emirates University, Al Ain, United Arab Emirates.
Hesham MesbahDpartment of Communication, Rollins College, Florida, USA.
Mokhtar ElareshiCommunication College, University of Sharjah, Sharjah, United Arab Emirates.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Misinformation remained a critical consideration during the COVID-19 that further cultivated fears leading to strong unrest among the public globally. This study clarifies certain misconceptions related to the pandemic by investigating whether factors such as altruism, entertainment, information-sharing, information-seeking, comprehensibility have impact on COVID-19 misinformation acceptance and sharing it in the UAE culture, with gender as a moderating factor. An online survey (google.com) was used, with a sample of 200 university students, and analyzed using PLS-SEM software to determine the effects of constructed factors. The findings indicated that entertainment, information-sharing, and information-seeking factors have impact on sharing COVID-19 misinformation, while comprehensibility has impact on acceptance of misinformation. Interestingly, gender was found to have no impact on all the constructed factors, suggesting that other moderating factors (e.g., age) need to be considered in future research. Generally, online users need to learn how to verify online information that they receive/share on other social media, especially regarding health concern.

Indexed as

AltruismCOVID-19EntertainmentInformation-seekingInformation-sharingMisinformation

Identifiers

PMID37810004
PMCPMC10558500

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.