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

2026-09-24

AI Summary

This study addresses the limited interpretability of existing fake news detection systems and their disconnect from communication theories. We propose a detection framework grounded in interdisciplinary theory that translates persuasion mechanisms into quantifiable features, thereby bridging social science and computational modeling. Methodologically, this work integrates statistical techniques, large language models, and multi-feature combination modeling, validating the predictive efficacy of these features on benchmark datasets. The proposed approach not only unifies automated detection with attribution-based explanation but also yields interpretable diagnostic signals for identifying misinformation. Ultimately, this research significantly enhances both the robustness and transparency of fake news detection systems.

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.

Problem

• fake news detection
• interpretability
• disinformation
• computational modeling
• interdisciplinary theories

Innovation

• Theory-informed computational framework
• Large language models
• Interpretable fake news detection
• Cross-disciplinary theories
• Feature quantification