Evidence map›Paper›PMID 39856045›Full record

ArticleNature communications2025

Differential impact from individual versus collective misinformation tagging on the diversity of Twitter (X) information engagement and mobility.

Junsol Kim, Zhao Wang, Haohan Shi, Hsin-Keng Ling, James Evans

Abstract read
In one paragraph

Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

5 authors.

Junsol KimDepartment of Sociology, University of Chicago, Chicago, IL, USA.ORCID http://orcid.org/0000-0003-1065-6479
Zhao WangComputational Social Science, University of Chicago, Chicago, IL, USA.
Haohan ShiSchool of Communication, Northwestern University, Evanston, IL, USA.ORCID http://orcid.org/0009-0009-9611-7732
Hsin-Keng LingDepartment of Sociology, University of Michigan, Ann Arbor, MI, USA.
James EvansDepartment of Sociology, University of Chicago, Chicago, IL, USA. jevans@uchicago.edu.ORCID http://orcid.org/0000-0001-9838-0707

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Fears about the destabilizing impact of misinformation online have motivated individuals and platforms to respond. Individuals have increasingly challenged others' online claims with fact-checks in pursuit of a healthier information ecosystem and to break down echo chambers of self-reinforcing opinion. Using Twitter (now X) data, here we show the consequences of individual misinformation tagging: tagged posters had explored novel political information and expanded topical interests immediately prior, but being tagged caused posters to retreat into information bubbles. These unintended consequences were softened by a collective verification system for misinformation moderation. In Twitter's new feature, Community Notes, misinformation tagging was peer-reviewed by other fact-checkers before revelation to the poster. With collective misinformation tagging, posters were less likely to retreat from diverse information engagement. Detailed comparison demonstrated differences in toxicity, sentiment, readability, and delay in individual versus collective misinformation tagging messages. These findings provide evidence for differential impacts from individual versus collective moderation strategies on the diversity of information engagement and mobility across the information ecosystem.

Indexed as

CommunicationInformation DisseminationSocial MediaHumans

Identifiers

PMID39856045
PMCPMC11760358

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.