Spectral Analysis of Fake News Propagation

2026-04-18

arXiv.org

AI Summary

This work addresses the limitation of existing fake news detection methods, which often rely on ad hoc topological features and lack a unified modeling of propagation structures. Drawing upon spectral graph theory, the study establishes rigorous spectral bounds that characterize the relationship between information diffusion graphs and their structural properties, introducing several novel spectral bounds integrated into a unified spectral representation. Furthermore, the authors design a discrete structure optimization framework that combines first-order perturbation approximation with bound-guided objectives to enable interpretable learning of propagation patterns. Experimental results demonstrate a significant spectral-domain distinction between fake and real news, and the proposed method achieves competitive classification performance on real-world datasets while uncovering interpretable trajectories of propagation dynamics.

Abstract

The propagation structure of fake news has been shown to be an important cue for detecting it; yet, existing propagation-based fake news detection methods have mainly relied on ad hoc topological features, and a unified view of cascade patterns is still lacking. To address this, we study news propagation from a spectral view by connecting graph spectra to propagation-related structural properties through rigorous spectral bounds. In particular, we introduce several new bounds and integrate them with existing ones into a unified spectral representation of information propagation. We then use these spectral bounds for downstream classification and design a discrete structural optimization framework to interpret learned propagation patterns. For efficient optimization, we rely on a first-order perturbation approximation and consider both score-guided and bound-guided objectives. Experiments on real-world data reveal meaningful spectral differences between fake and real news, competitive classification performance from spectral bounds, and interpretable evolution trajectories from structural optimization. The findings demonstrate the value of spectral analysis for understanding and modeling news propagation.

Problem

• fake news
• propagation structure
• spectral analysis
• information cascades
• graph spectra

Innovation

• spectral analysis
• fake news detection
• propagation structure
• spectral bounds
• structural optimization