A rumor heat prediction model based on rumor and anti-rumor multiple messages and knowledge representation

In the field of online social networks, the effective prediction of group behavior is the key to fitting the trajectory of rumor topic propagation. Considering the heterogeneity and complexity of a rumor topic propagation network, this paper proposes a rumor heat prediction model based on multiple rumor and anti-rumor messages and knowledge representation. Firstly, aiming at the dynamic interaction of multiple messages under the rumor topic network, this paper introduces evolutionary game theory to quantify the influence of multiple messages on group behavior to truly reflect the relationship structure of the rumor topic spreading network. Secondly, to solve the difficulties in mining the complex features of multisource entities, knowledge representation is considered the theoretical basis to map the heterogeneous topic network to the feature vector space and learn the low-rank vector representation of the complex features. Finally, considering the time-sensitive nature of rumor propagation and the advantages of graph neural networks in processing non-Euclidean structured data, the dynamic rumor topic heat prediction model called K-GraphSAGE, which is based on group behavior, is proposed. The experiment results show that this method can not only effectively predict the group behavior and the heat of the rumor topics, but also unearth the mechanism behind the spreading law of the rumor topics.