Data Fusion Oriented Graph Convolution Network Model for Rumor Detection

Abstract

With the rapid development of Mobile Internet, social media platforms explosively grow and greatly facilitate people to obtain and exchange information. Since any users can post arbitrary information on social media, some people with ulterior motives try to create and spread various misinformation for their own benefits, which might damage citizen rights and disturb social order. In this article, we propose a novel rumor detection model based on Graph Convolution Networks (GCNs). Based on the real dataset from social media, we consider not only the static features such as users’s basic information and text contents, but also the dynamic features such as rumor propagation relations. The GCN-based model is used to represent the spreading structure of rumors, with graph convolution operator for node vector updating. We also optimize the feature fusion module and pooling module to make our model have better performance. Experiments on Sina Weibo dataset validate the performance of the propsed GCN-based model for rumor detection.