Journal Article

Graph Convolutional-Based Deep Residual Modeling for Rumor Detection on Social Media

Ye, Na, Zhou, Yijie, Zhang, Suiyu, Yu, Dingguo, Shang, Ke-ke

2023

Overview

The popularity and development of social media have made it more and more convenient to spread rumors, and it has become especially important to detect rumors in massive amounts of information. Most of the traditional rumor detection methods use the rumor content or propagation structure to mine rumor characteristics, ignoring the fusion characteristics of the content and structure and their interaction. Therefore, a novel rumor detection method based on heterogeneous convolutional networks is proposed. First, this paper constructs a heterogeneous map that combines both the rumor content and propagation structure to explore their interaction during rumor propagation and obtain a rumor representation. On this basis, this paper uses a deep residual graph convolutional neural network to construct the content and structure interaction information of the current network propagation model. Finally, this paper uses the Twitter15 and Twitter16 datasets to verify the proposed method. Experimental results show that the proposed method has higher detection accuracy compared to the traditional rumor detection method.

• Publisher: MDPI AG
• Subject: Accuracy / Analysis / Artificial neural networks / Deep learning / Digital media / False information / false information detection / Gossip / graph neural network / Graph neural networks / Graph representations / Machine learning / Neural networks / News media / residual structure / Social media / Social networks