Evidence map›Paper›PMID 42086582›Full record

ArticleNature communications2026

Community-based fact-checking reduces the spread of misleading posts on X (formerly Twitter).

Yuwei Chuai, Moritz Pilarski, Thomas Renault, David Restrepo-Amariles, Aurore Troussel-Clément, Gabriele Lenzini, Nicolas Pröllochs

Abstract read
In one paragraph

Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

7 authors.

Yuwei ChuaiUniversity of Luxembourg, Luxembourg, Luxembourg.ORCID 0000-0001-6181-7311
Moritz PilarskiJLU Giessen, Giessen, Germany.ORCID 0009-0009-5063-0979
Thomas RenaultUniversité Paris-Saclay, Paris, France.
David Restrepo-AmarilesHEC Paris, Paris, France.
Aurore Troussel-ClémentHEC Paris, Paris, France.
Gabriele LenziniUniversity of Luxembourg, Luxembourg, Luxembourg.ORCID 0000-0001-8229-3270
Nicolas PröllochsJLU Giessen, Giessen, Germany. nicolas.proellochs@wi.jlug.de.ORCID 0000-0002-1835-7302

Funding

Deutsche Forschungsgemeinschaft (German Research Foundation) 492310022Fonds National de la Recherche Luxembourg (National Research Fund) INTER_FNRS_21_16554939_REMEDIS
6 · The paper itself

Abstract

Community-based fact-checking is a promising approach to correct misleading posts at scale. Yet, causal evidence regarding its effectiveness in reducing the spread of misinformation on social media is missing. Here, we perform a large-scale empirical study to analyze whether community notes reduce the spread of misleading posts on X (formerly Twitter). Using a Difference-in-Differences design and repost time series data for N = 237,180 (community fact-checked) cascades that have been reposted more than 431 million times, we find that exposing users to community notes reduces the subsequent spread of misleading posts by, on average, 61.2%. The effect is pronounced across the board but significantly weaker for posts from influential accounts and political content. Additionally, community notes increase the odds that users delete their misleading posts by 94.3%. Although community notes are broadly effective in reducing the spread of posts once annotated, they often appear too late to intervene in the early (and most viral) stage of the diffusion. As a result, their system-wide effect is more modest, lowering total engagement with misleading posts by 14.9%. Our work provides important insights that can inform future initiatives aimed at increasing the effectiveness of community-based fact-checking approaches on social media.

Indexed as

CommunicationInformation DisseminationSocial MediaHumans

Identifiers

PMID42086582
PMCPMC13144318

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.