A Predictive Model for Early Intervention Efficacy of Fake News based on Epistemic Vigilance
DOI:
https://doi.org/10.6981/FEM.202604_7(4).0007Keywords:
Fake News; Epistemic Vigilance; Truth-Default Theory; Early Intervention; Natural Language Processing; Design Science.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.
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