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

Xi Xiao, Zeming Wu, Chengzong Cai, Tian Bian, Guangwu Hu, Qing Li, Cheng Huang

Abstract: 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.

External IDs:dblp:journals/eaai/XiaoWCBHLH25