VGA: Vision and Graph Fused Attention Network for Rumor Detection

TL;DR

This paper introduces VGA, a novel multimodal rumor detection network that leverages propagation structures, visual tampering features, and hidden textual information in images to improve detection accuracy.

Contribution

The study proposes a new fused attention network that incorporates propagation structures and visual-textual features for enhanced rumor detection.

Findings

VGA outperforms existing methods on three datasets.

Incorporating propagation structures improves detection accuracy.

Visual tampering features and hidden textual info are crucial for rumor detection.

Abstract

With the development of social media, rumors have been spread broadly on social media platforms, causing great harm to society. Beside textual information, many rumors also use manipulated images or conceal textual information within images to deceive people and avoid being detected, making multimodal rumor detection be a critical problem. The majority of multimodal rumor detection methods mainly concentrate on extracting features of source claims and their corresponding images, while ignoring the comments of rumors and their propagation structures. These comments and structures imply the wisdom of crowds and are proved to be crucial to debunk rumors. Moreover, these methods usually only extract visual features in a basic manner, seldom consider tampering or textual information in images. Therefore, in this study, we propose a novel Vision and Graph Fused Attention Network (VGA) for rumor detection to utilize propagation structures among posts so as to obtain the crowd opinions and further explore visual tampering features, as well as the textual information hidden in images. We conduct extensive experiments on three datasets, demonstrating that VGA can effectively detect multimodal rumors and outperform state-of-the-art methods significantly.