A novel high-accuracy graph neural network-based rumor detection method

Rumors spreading on social media platforms result in potential damages. A precise rumor detection mechanism can help form a healthy public opinion environment. In recent years, deep learning-based rumor detection methods, especially graph model-based ones, have risen and reached promising performance. However, there are several defects in existing methods, which limit models from efficiently utilizing the propagation structure. In this paper, we propose a novel rumor detection model, which has high accuracy and reaches state-of-the-art performance. First, we design a powerful comprehensive rumor feature extractor that explicitly overcomes the restriction of previous Graph Neural Networks-based models. Then, by introducing Kernel Subtree features, our model acquires the capability to learn crucial local features from important nodes. Comparative experiments performed on two real-world social media platforms demonstrate that our work reaches state-of-the-art performance, which outperforms the best baseline with 1. 6% and 1. 9% in accuracy respectively.

Authors

• Xi Xiao Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China Peng Cheng Laboratory, Shenzhen, China Key Laboratory of Cyberspace Security, Ministry of Education of China, Zhengzhou, China
• Zeming Wu
• Chengzong Cai
• Tian Bian
• Guangwu Hu
• Qing Li The Hong Kong Polytechnic University
• Cheng Huang Fudan University

Keywords

• Social media
• Graph convolutional networks
• Rumor detection
• Artificial Intelligence applications
• Deep learning implementation