Open Access

ARTICLE

Research on Prediction Methods of Prevalence Perception under Information Exposure

by Weijin Jiang, Fang Ye, Wei Liu, Xiaoliang Liu, Guo Liang, Yuhui Xu, Lina Tan

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: .

Computers, Materials & Continua 2020, 65(3), 2263-2275. https://doi.org/10.32604/cmc.2020.010082

Received 10 February 2020; Accepted 29 May 2020; Issue published 16 September 2020

Abstract

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.

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Citations