A Diffusion Model for Multimessage Multidimensional Complex Game Based on Rumor and Anti-Rumor

In online social networks, rumors have become a flashpoint for public opinion. This article proposes a rumor and anti-rumor diffusion model based on multi-information multidimensional compound game considering the complex game antagonism in the process of spreading multiple messages. First, in view of the diversity and complexity of the characteristic space of communication individual, communication structure, and multitype rumor and anti-rumor news, the communication network is expressed in low-dimensional, real-value, and dense vectorization by combining the representation learning algorithm and centering on the content, structure, and attribute of information transmission. Second, considering the antagonistic and competitive relationship between multiple rumors and anti-rumors, and considering the compound iterative cascade of multi-information rumor, this article proposes a multi-information rumor and anti-rumor game model from a multidimensional perspective. Finally, the information expression of the topic space and the complex game relationship between multiple messages are considered comprehensively. At the same time, considering the graph convolutional network (GCN)’s ability of convolution processing non-Euclidean data such as social network, a dynamic, unified representation, multimessage complex game topic propagation model MMGameGCN is proposed based on graph convolutional neural network (MultiMessage-game GCN). The experiment shows that the model can more truly and effectively reflect the process of network rumor propagation in the multimessage transmission network, effectively analyze the user group behavior of rumor topic under multiple messages, and correctly perceive the spread situation of rumor and anti-rumor.