Group behavior dissemination model of social hotspots based on data enhancement and data representation

Abstract

The emergence and dissemination of hotspots in social networks mainly depend on the participation of group users. In this paper, considering the sparsity and complexity of effective data, we propose a group behavior dissemination model on the basis of data enhancement and data representation. First, given the inaccurate prediction results caused by the sparsity of valid data and the advantages of Generative Adversarial Networks (GAN) in learning data distribution and enhancing data, GAN is introduced to generate homomorphic data. We found that the accuracy improved by at least 6%. Second, with the diversity and complexity of the hotspot feature space and the ability of representation learning to mine hidden features of the hotspot, we designed a new method, called HP2vec, convert the feature space to a low rank and dense vector. Finally, considering the dynamic time limit of hotspot spreading, we create time slices to discretize the life of a hotspot and propose a dynamic dissemination method based on CNN. The experimental section shows that the accuracy of this method is as high as 86% in Weibo dataset and as high as 93% in twitter dataset. The model alleviates data sparseness and effectively predicts the group behavior in hotspots.