Evidence map›Paper›PMID 41748641›Full record

ArticleNature communications2026

Rumor propagation on hypergraphs.

Kleber Andrade Oliveira, Pietro Traversa, Guilherme Ferraz de Arruda, Yamir Moreno

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. Rumor propagation on hypergraphs.Nature communications · 2026
    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

4 authors.

Kleber Andrade Oliveira *Social Dynamics Research Lab, Department of Psychology, University of Limerick, Limerick, Ireland.ORCID http://orcid.org/0000-0002-0464-7050
Pietro Traversa *Institute for Biocomputation and Physics of Complex Systems (BIFI), University of Zaragoza, Zaragoza, Spain.ORCID http://orcid.org/0009-0004-9804-3769
Guilherme Ferraz de ArrudaInstitute of Physics, Gleb Wataghin, University of Campinas (UNICAMP), Campinas, São Paulo, Brazil.ORCID http://orcid.org/0000-0003-1647-5126
Yamir MorenoInstitute for Biocomputation and Physics of Complex Systems (BIFI), University of Zaragoza, Zaragoza, Spain. yamir@unizar.es.ORCID http://orcid.org/0000-0002-0895-1893

Funding

EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 European Research Council (H2020 Excellent Science - European Research Council) 101124175
6 · The paper itself

Abstract

The rapid spread of information and rumors through social media platforms, especially in group settings, motivates the need for more sophisticated models of rumor propagation. Traditional pairwise models do not account for group interactions, a limitation that we address by proposing a higher-order rumor model based on hypergraphs. Our model incorporates a group-based annihilation mechanism, where a spreader becomes a stifler when the fraction of hyperedges aware of the rumor exceeds a threshold. The dynamics has two distinct subcritical behaviors: exponential and power-law decay, which can coexist depending on the heterogeneity of the hypergraph. Interestingly, in the set of parameters we analysed, we found continuous phase transitions in both homogeneous and heterogeneous hypergraphs. This finding aligns with the literature suggesting that real-world rumor propagation occurs near criticality. Finally, we validated our model using empirical data from Telegram and email cascades, which provides additional evidence and possible explanations for this criticality claim. These results open the door to a more detailed understanding of rumor dynamics in higher-order systems.

Identifiers

PMID41748641
PMCPMC13061991

What OpenQuestion holds

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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.