A Social Topic Diffusion Model Based on Rumor, Anti-Rumor, and Motivation-Rumor

Abstract

The spread of online rumor poses challenges to social peace and public order. Traditional research on rumor diffusion commences from the rumor itself, without considering the symbiosis and confrontation of anti-rumor and motivation-rumor. This study proposed a diffusion method for online rumor based on three messages: rumor, anti-rumor, and motivation-rumor. First, considering the ability of representation learning to learn unsupervised features, we decided to use representation learning method to the diversity and complexity of the content and structure feature space. In particular, we designed a new representation method—Rumor2vec—for the potential structural feature of the rumor diffusion network. Second, considering the mutual promotion and suppression of the three messages, we constructed a new network topology using the cooperative and competitive relationships based on the evolutionary game theory. Finally, considering the ability of graph convolutional network (GCN) to convolute non-Euclidean structure data such as social network, and in view of the time effectiveness of topic evolution, this study proposed a dynamic and game-GCN (evolutionary game theory GCN)-based rumor diffusion model. Experiments show that the model can not only predict the group behavior under the rumor topic but also accurately reflect the cooperation and competition among multiple messages.