Large-scale analysis of fact-checked stories on Twitter reveals graded effects of ambiguity and falsehood on information reappearance.

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 ( 13.76 times as likely as a null model), indicating both

SUBMITTER: Kauk J 

PROVIDER: S-EPMC11837328 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature