Evidence map›Paper›PMID 40966290›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2025

Community notes reduce engagement with and diffusion of false information online.

Isaac Slaughter, Axel Peytavin, Johan Ugander, Martin Saveski

Erratum issuedAbstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Understanding the strengths and limitations of community-based responses to misinformation.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  4. Community notes reduce engagement with and diffusion of false information online.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  5. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Isaac SlaughterInformation School, University of Washington, Seattle, WA 98195.ORCID 0000-0002-1911-2374
Axel PeytavinDepartment of Management Science and Engineering, Stanford University, Stanford, CA 94305.ORCID 0000-0001-6905-4099
Johan UganderDepartment of Management Science and Engineering, Stanford University, Stanford, CA 94305.
Martin SaveskiInformation School, University of Washington, Seattle, WA 98195.ORCID 0000-0002-9908-7454

Funding

DOD | USA | AFC | CCDC | Army Research Office (ARO) W911NF-20-1-0252
6 · The paper itself

Abstract

Social networks scaffold the diffusion of information on social media. Much attention has been given to the spread of true vs. false content on online social platforms, including the structural differences between their diffusion patterns. However, much less is known about how platform interventions on false content alter the engagement with and diffusion of such content. In this work, we estimate the causal effects of Community Notes, a novel fact-checking feature adopted by X (formerly Twitter) to solicit and vet crowd-sourced fact-checking notes for false content. We gather detailed time series data for 40,078 posts for which notes have been proposed and use synthetic control methods to estimate a range of counterfactual outcomes. We find that attaching fact-checking notes significantly reduces the engagement with and diffusion of false content. We estimate that, on average, the notes resulted in reductions of 46.1% in reposts, 44.1% in likes, 21.9% in replies, and 13.5% in views after being attached. Over the posts' entire lifespans, these reductions amount to 11.6% fewer reposts, 13.3% fewer likes, 6.9% fewer replies, and 5.5% fewer views on average. In reducing reposts, we observe that diffusion cascades for fact-checked content are less deep and less "viral," but not less broad, than synthetic control estimates for non-fact-checked content with similar reach. This structural difference contrasts notably with differences between false vs. true content diffusion itself, where false information diffuses farther, but with structural patterns that are otherwise indistinguishable from those of true information, conditional on reach.

Indexed as

Information DisseminationSocial MediaSocial NetworkingHumanscrowd-sourced fact-checkinginformation diffusionmisinformation

Identifiers

PMID40966290
PMCPMC12478135

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.