Evidence map›Paper›PMID 41798732›Full record

ArticlePNAS nexus2026

Beliefs and sharing intentions of human- and AI-generated fake news: Evidence from 27 European countries.

Ádám Stefkovics, Dömötör Gere

Abstract read
In one paragraph

Article in PNAS nexus, 2026. 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

2 authors.

Ádám StefkovicsSzazadveg Foundation, Political Analysis Research Institute, 1037 Budapest, Hungary.ORCID https://orcid.org/0000-0003-4961-7792
Dömötör GereSzazadveg Foundation, Political Analysis Research Institute, 1037 Budapest, Hungary.ORCID https://orcid.org/0009-0003-7917-5865

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Misinformation remains a major challenge in today's information environment, and rapid advances in AI-driven content generation risk amplifying this problem. Generative AI represents a double-edged sword: beyond its growing utility for detecting misinformation, it can also facilitate democratic deliberation, counter conspiracy narratives, and promote reliable information, even as the same technologies enable the rapid, large-scale production of persuasive false content. Understanding how people perceive AI-generated misinformation is therefore crucial for designing effective interventions and safeguarding information integrity. To address this, we embedded a preregistered experiment in a large-scale web survey conducted across 27 European countries. Participants were presented with eight short news headlines related to the Russo-Ukrainian war: four AI-generated and four human-generated, evenly split between real and fake news. For each headline, respondents assessed its perceived veracity and their willingness to share it. Our findings show that fake news is consistently viewed as less accurate and less likely to be shared, with systematic differences across countries and individual characteristics such as cognitive reflection, ideology, and trust. While differences between human- and AI-generated content were minimal, the results reveal broader and robust patterns in how people evaluate misinformation across diverse European contexts. These insights highlight the need to strengthen individuals' cognitive and informational resilience to counter the spread of misleading content in increasingly complex media environments.

Indexed as

AIfake newsmisinformationpsychological and cognitive sciencesRusso-Ukrainian warsocial sciences

Identifiers

PMID41798732
PMCPMC12964124

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.