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

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Tác giả: Julian Kauk, Helene Kreysa, Stefan R Schweinberger

Ngôn ngữ: eng

Ký hiệu phân loại: 516.6 Abstract descriptive geometry

Thông tin xuất bản: England : PNAS nexus , 2025

Mô tả vật lý:

Bộ sưu tập: NCBI

ID: 470806

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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