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arXiv Machine Learning By Zhaoyang Cao, Miriam Metzger, Reza Zafarani

Fake News Theories: Harnessing Disciplinary Insights for Computational Modeling, Detection, and Explanation

Read the original on arXiv Machine Learning →

The paper presents a theory-informed computational framework that converts cross-disciplinary theories of fake news into measurable features for automated detection and explanation. By reviewing theories from social sciences, psychology, economics, and more, the authors establish a broad theoretical foundation for computational modeling. Experiments on benchmark datasets demonstrate that theory-derived features are predictive, provide interpretable diagnostic signals, and that multi-feature models generally outperform individual features, though gains are modest.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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