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Research on Prediction Methods of Prevalence Perception under Information Exposure

Weijin Jiang1, 2, 3, 4, Fang Ye1, 2, *, Wei Liu2, 3, Xiaoliang Liu1, 2, Guo Liang5, Yuhui Xu2, 3, Lina Tan1, 2

1 School of Computer and Information Engineering, Hunan University of Technology and Business, Changsha, 410205, China.
2 Key Laboratory of Hunan Province for New Retail Virtual Reality Technology, Hunan University of Technology and Business, Changsha, 410205, China.
3 Institute of Big Data and Internet Innovation, Mobile E-Business Collaborative Innovation Center of Hunan Province, Hunan University of Technology and Business, Changsha, 410205, China.
4 School of Computer Science and Technology, Wuhan University of Technology, Wuhan, 430073, China.
5 School of Bioinformatics, University of Minnesota, Twin Cities, USA.

* Corresponding Author: Fang Ye. Email: email.

Computers, Materials & Continua 2020, 65(3), 2263-2275.


With the rapid development of information technology, the explosive growth of data information has become a common challenge and opportunity. Social network services represented by WeChat, Weibo and Twitter, drive a large amount of information due to the continuous spread, evolution and emergence of users through these platforms. The dynamic modeling, analysis, and network information prediction, has very important research and application value, and plays a very important role in the discovery of popular events, personalized information recommendation, and early warning of bad information. For these reasons, this paper proposes an adaptive prediction algorithm for network information transmission. A popularity prediction algorithm is designed to control the transmission trend based on the gray Verhulst model to analyze the law of development and capture popular trends. Experimental simulations show that the proposed perceptual prediction model in this paper has a better fitting effect than the existing models.


Cite This Article

W. Jiang, F. Ye, W. Liu, X. Liu, G. Liang et al., "Research on prediction methods of prevalence perception under information exposure," Computers, Materials & Continua, vol. 65, no.3, pp. 2263–2275, 2020.


cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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