Linguistic-style-aware Neural Networks for Fake News Detection

TL;DR

This paper introduces HERO, a hierarchical recursive neural network that models linguistic style through hierarchical linguistic trees to improve fake news detection, outperforming existing methods on real-world datasets.

Contribution

It is the first to integrate hierarchical linguistic trees with neural networks for fake news detection, capturing recursive linguistic structures effectively.

Findings

HERO outperforms state-of-the-art techniques in classifying news documents.

The method reveals patterns in linguistic style differences between fake and real news.

Hierarchical linguistic trees effectively encode stylistic features for classification.

Abstract

We propose the hierarchical recursive neural network (HERO) to predict fake news by learning its linguistic style, which is distinguishable from the truth, as psychological theories reveal. We first generate the hierarchical linguistic tree of news documents; by doing so, we translate each news document's linguistic style into its writer's usage of words and how these words are recursively structured as phrases, sentences, paragraphs, and, ultimately, the document. By integrating the hierarchical linguistic tree with the neural network, the proposed method learns and classifies the representation of news documents by capturing their locally sequential and globally recursive structures that are linguistically meaningful. It is the first work offering the hierarchical linguistic tree and the neural network preserving the tree information to our best knowledge. Experimental results based on public real-world datasets demonstrate the proposed method's effectiveness, which can outperform state-of-the-art techniques in classifying short and long news documents. We also examine the differential linguistic style of fake news and the truth and observe some patterns of fake news. The code and data have been publicly available.