Evidence map›Paper›PMID 40476933›Full record

ArticleSmart learning environments2023

Fake news detection on social media: the predictive role of university students' critical thinking dispositions and new media literacy.

Ali Orhan

Abstract readNews
In one paragraph

Article in Smart learning environments, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 2 pooled it
–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

8 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
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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

1 author.

Ali OrhanSchool of Foreign Languages, Zonguldak Bülent Ecevit University, İncirharmanı Campus, Kozlu, Zonguldak, Turkey.ORCID 0000-0003-1234-3919

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study aimed to investigate the predictive role of critical thinking dispositions and new media literacies on the ability to detect fake news on social media. The sample group of the study consisted of 157 university students. Sosu Critical Thinking Dispositions Scale, New Media Literacy Scale, and fake news detection task were employed to gather the data. It was found that university students possess high critical thinking dispositions and new media literacies as well as high fake news detection abilities and there is a positive and moderate relationship among these variables. Also, this study revealed that critical thinking dispositions and new media literacies significantly predicted university students' abilities to detect fake news on social media and they together explained 18% of the total variance on fake news detection. Besides, university students' critical thinking dispositions presented a larger effect on their abilities to detect fake news than new media literacies.

Indexed as

Ability to detect fake newsCritical thinking dispositionsNew media literacySocial mediaUniversity students

Identifiers

PMID40476933
PMCPMC10130304

What OpenQuestion holds

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LicenceCC BY
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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.