4.7 Article

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

Journal

INFORMATION SCIENCES
Volume 617, Issue -, Pages 293-309

Publisher

ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2022.10.092

Keywords

Social networks; Hotspots; Information dissemination; Group behavior; Representation learning; Data enhancement

Funding

  1. National Natural Science Foundation of China
  2. Natural Science Foundation of Chongqing
  3. Science and Technology Research Program of Chongqing Municipal Education Commission
  4. Youth Innovation Group Support Program of ICE Discipline of CQUPT
  5. [62006032]
  6. [62072066]
  7. [CSTB2022NSCQ-MSX0329]
  8. [KJZD-K201900603]
  9. [SCIE-QN-2022-05]

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This paper proposes a group behavior dissemination model based on data enhancement and data representation. It introduces Generative Adversarial Networks (GAN) to generate homomorphic data, designs HP2vec method to convert the feature space to a low rank and dense vector, and proposes a dynamic dissemination method based on CNN. The experiments show that this model has high accuracy and can effectively predict group behavior in hotspots.
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.

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