Rumor Detection Based on Source Information and Gating Graph Neural Network

Social media not only brings convenience to people, but also provides a platform for spreading rumors. Currently, most rumor detection methods are based on text content information. However, in social media scenarios, text content is mostly short text, which often leads to poor performance due to data sparsity. Message propagation on social networks can be modeled as a graph structure. Previous studies have taken into account the characteristics of message propagation structure, and detected rumors through GCN. GCN aggregates neighbors based on structural information to enhance node representation, but some neighbor aggregation is useless and may even cause noise, which making the representation obtained from GCN unreliable. Meanwhile, these methods can not effectively highlight the importance of the source post information. In this paper, we propose a propagation graph convolution network model GUCNH. In GUCNH model, information forwarding graph is constructed first, and the representation of neighbor nodes is aggregated by two fusion gated convolution network modules. Fusion gating can select and combine the feature representation before and after the graph convolution to get a more reliable representation. Considering that in forwarding graph, any post may interact with each other rather than just with its neighbors, a multi-headed self-attention module is introduced between two integrated gated convolution network modules to model the multi-angle influence between posts. In addition, in forwarding graph, the source posts often contain the richest information than reposts. After generating each node representation, we selectively enhance the source node’s information to enhance the influence of the source posts. Experiments on three real datasets show that our proposed model outperforms the existing methods.