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A Predictive Model for Early Intervention Efficacy of Fake News based on Epistemic Vigilance

Yihan Zhao
Frontiers in Economics and Management, (2026), Vol.7, No.4, pp.51-58
Published: April 16, 2026
DOI: 10.6981/FEM.202604_7(4).0007
PDF: Download Full Text PDF
Abstract

Early automated intervention against fake news is critical for social media platform governance. This paper proposes a predictive method for intervention efficacy grounded in Epistemic Vigilance. First, using Truth-Default Theory (TDT) as a theoretical lens, empirical analysis on the RumourEval 2019 dataset confirms that "Deny" first-comments effectively break audiences' default inertia, significantly suppressing the subsequent support ratio to 0.525. Second, we translate this mechanism into a probability prediction task, utilizing the ELECTRA architecture and Focal Loss to address extreme sample imbalance. Cross-validation results demonstrate that our method substantially improves the recall rate of high-potential refutational texts to 0.898. By reversely applying stance evolution logic to active intervention evaluation, this study provides a scientific reference for platforms to deploy high-recall early-blocking strategies.

Keywords: Fake News; Epistemic Vigilance; Truth-Default Theory; Early Intervention; Natural Language Processing; Design Science.
APA Citation: Yihan Zhao (2026). A Predictive Model for Early Intervention Efficacy of Fake News based on Epistemic Vigilance. Frontiers in Economics and Management, 7(4), 51-58. https://doi.org/10.6981/FEM.202604_7(4).0007

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