Evidence map›Paper›PMID 40325023›Full record

ArticleScientific reports2025

Coordinated link sharing on Facebook.

Yunkang Yang, Ramesh Paudel, Jordan McShan, Matthew Hindman, H Howie Huang, David Broniatowski

Abstract read
In one paragraph

Article in Scientific reports, 2025. 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

6 authors.

Yunkang YangTexas A&M University, College Station, 77843, USA. yunkangyang@tamu.edu.
Ramesh PaudelGeorge Washington University, Washington, 20052, USA.
Jordan McShanGeorge Washington University, Washington, 20052, USA.
Matthew HindmanGeorge Washington University, Washington, 20052, USA.
H Howie HuangGeorge Washington University, Washington, 20052, USA.
David BroniatowskiGeorge Washington University, Washington, 20052, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Malicious actors regularly attempt to manipulate social media using coordinated posting. Many existing methods for detecting this coordination, though, have relied primarily on post-timing, which is trivially easy to change. In this paper, we make a significant methodological advancement in coordination detection, leveraging highly regular statistical patterns in the speed and frequency of sharing. We apply and validate this approach on Facebook, using 11.2 million link posts from a list of 16,169 most popular English-language Facebook pages that referenced at least one of the top eight US politicians in any post, a set of pages that produced more than 91% of all user engagement in this category during our collection period. Our approach can be calibrated and adapted across contexts, platforms, and times, allowing researchers to build valid, testable, but still human-interpretable models of platform manipulations.

Indexed as

CoordinationFacebookSocial media

Identifiers

PMID40325023
PMCPMC12053595

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.