Frontiers of Data and Computing ›› 2023, Vol. 5 ›› Issue (1): 115-127.

CSTR: 32002.14.jfdc.CN10-1649/TP.2023.01.011

doi: 10.11871/jfdc.issn.2096-742X.2023.01.011

• Technology and Application • Previous Articles     Next Articles

Research on Optimization and Intervention of SIR Model of Network Public Opinion

YANG Chaobo1(),XIE Weihong1,2,WANG Ligang2,3,*()   

  1. 1. School of Management, Guangdong University of Technology, Guangzhou, Guangdong 510623, China
    2. School of Economics, Guangdong University of Technology, Guangzhou, Guangdong 510623, China
    3. Department of Education, Guangdong University of Technology, Guangzhou, Guangdong 510006, China
  • Received:2022-08-03 Online:2023-02-20 Published:2023-02-20

Abstract:

[Background] Network public opinion has an increasingly important influence on the healthy development of the enterprise. The SIR infectious disease model is a commonly used research model for network public opinion dissemination. At present, most researches on network public opinion dissemination are based on the SIR model and its variants. The SIR model does not subdivide infected persons, which is not conducive to propagation research and the precise monitoring of network public opinion. [Objective] By optimizing the SIR model, it can better reflect the real situation of enterprise network public opinion and improve the monitoring effect. [Methods] The work presented in this article divides the infected persons under the SIR model into three types: positively infected, generally infected, and negatively infected. For different types of infected people, the posting rate is different. Differentiated posting rates are set to improve the monitoring and prediction accuracy of enterprise network public opinion. According to the different levels of network public opinion, three monitoring measures with different intervention levels are designed to enhance the monitoring effect. [Results] The improved model is applied to the real enterprise network public opinion of the "unqualified coliform group of Haidilao". we found that the monitoring effect of the improved model is more ideal than that of the SIR model. [Conclusions] Subdividing the research objects, considering the posting rate of infected persons, and formulating intervention levels with different supervision strengths will help us to improve the accuracy, supervision effectiveness, and prediction accuracy of enterprise network public opinion supervision.

Key words: network public opinion, infectious disease model, dissemination