ArticlePNAS nexus2025
Large-scale analysis of fact-checked stories on Twitter reveals graded effects of ambiguity and falsehood on information reappearance.
Article in PNAS nexus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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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.
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.
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Who cites it
0 citing papers in PubMed.
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Misinformation disrupts our information ecosystem, adversely affecting individuals and straining social cohesion and democracy. Understanding what causes online (mis)information to (re)appear is crucial for fortifying our information ecosystem. We analyzed a large-scale Twitter (now "X") dataset of about 2 million tweets across 123 fact-checked stories. Previous research suggested a falsehood effect (false information reappears more frequently) and an ambiguity effect (ambiguous information reappears more frequently). However, robust indicators for their existence remain elusive. Using polynomial statistical modeling, we compared a falsehood model, an ambiguity model, and a dual effect model. The data supported the dual effect model (
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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.