Graph with Sequence: Broad-Range Semantic Modeling for Fake News Detection

TL;DR

This paper introduces BREAK, a novel semantic modeling approach for fake news detection that uses a denoising graph-based framework to capture broad-range semantics while minimizing structural and feature noise, leading to improved detection accuracy.

Contribution

The paper presents BREAK, a new model that combines a fully connected semantic graph with dual denoising modules, effectively capturing comprehensive semantics and reducing noise for fake news detection.

Findings

BREAK outperforms existing methods on four datasets.

The dual denoising modules effectively reduce structural and feature noise.

The bi-level optimization enhances semantic representation quality.

Abstract

The rapid proliferation of fake news on social media threatens social stability, creating an urgent demand for more effective detection methods. While many promising approaches have emerged, most rely on content analysis with limited semantic depth, leading to suboptimal comprehension of news content.To address this limitation, capturing broader-range semantics is essential yet challenging, as it introduces two primary types of noise: fully connecting sentences in news graphs often adds unnecessary structural noise, while highly similar but authenticity-irrelevant sentences introduce feature noise, complicating the detection process. To tackle these issues, we propose BREAK, a broad-range semantics model for fake news detection that leverages a fully connected graph to capture comprehensive semantics while employing dual denoising modules to minimize both structural and feature noise. The semantic structure denoising module balances the graph's connectivity by iteratively refining it between two bounds: a sequence-based structure as a lower bound and a fully connected graph as the upper bound. This refinement uncovers label-relevant semantic interrelations structures. Meanwhile, the semantic feature denoising module reduces noise from similar semantics by diversifying representations, aligning distinct outputs from the denoised graph and sequence encoders using KL-divergence to achieve feature diversification in high-dimensional space. The two modules are jointly optimized in a bi-level framework, enhancing the integration of denoised semantics into a comprehensive representation for detection. Extensive experiments across four datasets demonstrate that BREAK significantly outperforms existing fake news detection methods.