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This research advances fake news detection by integrating social science theories into large language models, creating interpretable features that bridge the gap between human judgment and automated systems. While the performance gains from specific feature combinations are modest, the framework establishes a robust theoretical foundation for building more transparent and human-centered disinformation tools. By grounding computational models in established disciplines like psychology and economics, this work addresses critical interpretability issues in current AI-driven detection pipelines.
Fake News Theories: Harnessing Disciplinary Insights for Computational Modeling, Detection, and Explanation
arXiv:2609.30427v1 Announce Type: new Abstract: Disinformation research has produced increasingly accurate automated fake-news detectors, but many systems remain difficult to interpret and are weakly connected to established theories of persuasion, credibility, and human judgment. In this paper, we develop a theory-informed computational framework that translates cross-disciplinary theories of fake news into measurable features for automated detection and explanation through statistical techniques and large language models. To that end, we conduct a structured cross-disciplinary review of theories from social sciences, psychology, economics, among other disciplines that reveal how fake news persuades and spreads, thereby establishing a broad theoretical foundation for computational modeling. Experiments on benchmark datasets show that theory-derived features are predictive and provide interpretable, theory-referenced diagnostic signals. Multi-feature models generally outperform individual features, although gains among the strongest small feature combinations are modest. Our work highlights the value of interdisciplinary perspectives in building robust and interpretable fake news detection systems, advancing the foundation for human-centered approaches in combating disinformation.
Abstract:Disinformation research has produced increasingly accurate automated fake-news detectors, but many systems remain difficult to interpret and are weakly connected to established theories of persuasion, credibility, and human judgment. In this paper, we develop a theory-informed computational framework that translates cross-disciplinary theories of fake news into measurable features for automated detection and explanation through statistical techniques and large language models. To that end, we conduct a structured cross-disciplinary review of theories from social sciences, psychology, economics, among other disciplines that reveal how fake news persuades and spreads, thereby establishing a broad theoretical foundation for computational modeling. Experiments on benchmark datasets show that theory-derived features are predictive and provide interpretable, theory-referenced diagnostic signals. Multi-feature models generally outperform individual features, although gains among the strongest small feature combinations are modest. Our work highlights the value of interdisciplinary perspectives in building robust and interpretable fake news detection systems, advancing the foundation for human-centered approaches in combating disinformation.
Machine Learning (cs.LG); Computers and Society (cs.CY)
arXiv:2609.30427 [cs.LG]
(or arXiv:2609.30427v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2609.30427 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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