Spectral Analysis of Fake News Propagation

TL;DR

This paper introduces a spectral analysis framework for fake news propagation, providing new bounds and representations that improve detection and interpretability of news spread patterns.

Contribution

It develops a unified spectral approach to analyze propagation structures, introduces new spectral bounds, and applies them to classify fake news effectively.

Findings

Spectral differences effectively distinguish fake from real news.

Spectral bounds achieve competitive classification performance.

Structural optimization reveals interpretable propagation trajectories.

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.