MVAN: Multi-View Attention Networks for Fake News Detection on Social Media

TL;DR

This paper introduces MVAN, a neural network model that detects fake news on social media by analyzing source tweet content and propagation structure, outperforming existing methods and providing explanations.

Contribution

The paper proposes a novel Multi-View Attention Network (MVAN) that captures both textual and structural clues for fake news detection in realistic scenarios.

Findings

MVAN outperforms state-of-the-art methods by 2.5% in accuracy.

MVAN effectively identifies key clue words and suspicious users.

The model provides reasonable explanations for its predictions.

Abstract

Fake news on social media is a widespread and serious problem in today's society. Existing fake news detection methods focus on finding clues from Long text content, such as original news articles and user comments. This paper solves the problem of fake news detection in more realistic scenarios. Only source shot-text tweet and its retweet users are provided without user comments. We develop a novel neural network based model, \textbf{M}ulti-\textbf{V}iew \textbf{A}ttention \textbf{N}etworks (MVAN) to detect fake news and provide explanations on social media. The MVAN model includes text semantic attention and propagation structure attention, which ensures that our model can capture information and clues both of source tweet content and propagation structure. In addition, the two attention mechanisms in the model can find key clue words in fake news texts and suspicious users in the propagation structure. We conduct experiments on two real-world datasets, and the results demonstrate that MVAN can significantly outperform state-of-the-art methods by 2.5\% in accuracy on average, and produce a reasonable explanation.