Evidence map›Paper›PMID 42160358›Full record

ArticlePloS one2026

Trust the crowd: Crowdsourced fact-checking is as effective at reducing confidence in misinformation as expert fact-checking.

Cindy Phan Vu, Lauren L Saling

Abstract read
In one paragraph

Article in PloS one, 2026. 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

2 authors.

Cindy Phan VuSchool of Health and Biomedical Sciences, RMIT University, Melbourne, Australia.
Lauren L SalingSchool of Health and Biomedical Sciences, RMIT University, Melbourne, Australia.ORCID https://orcid.org/0000-0003-0722-6232

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The rapid spread of misinformation on social media has created significant challenges for expert fact-checking initiatives to counter in a timely and effective manner. Misinformation undermines behaviour and decision-making in many spheres including health and political domains. X (formerly known as 'Twitter') utilises crowdsourced fact-checking (termed 'Community Notes') to manage the high volume of and engagement with online misinformation. Community Notes have also been introduced to mitigate perceived partisanship and bias of expert fact-checkers. The present study recruited 102 participants to investigate whether expert or crowdsourced fact-checks on X are more effective at reducing belief in misinformation and engagement with misinformation. Participants were randomly allocated into either an expert or crowdsourced fact-checking condition. Confidence in the veracity of misinformation and willingness to retweet were measured, before and after exposure to fact-checks. It was found that both crowdsourced and expert fact-checks reduced confidence in misinformation and willingness to retweet the information. The results demonstrate the efficacy of crowdsourced fact-checking, a fact-checking variant that is rapidly gaining popularity. Given this, the adoption of crowdsourced fact-checking by other social media platforms warrants consideration.

Indexed as

CommunicationCrowdsourcingSocial MediaTrustAdultDigital MediaFemaleHumansMaleYoung Adult

Identifiers

PMID42160358
PMCPMC13189315

What OpenQuestion holds

Textmetadata
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.