Direct immune-SCIR public-opinion propagation model based on real-time online users

doi:10.1088/1674-1056/aba9c0

Abstract

Current public-opinion propagation research usually focused on closed network topologies without considering the fluctuation of the number of network users or the impact of social factors on propagation. Thus, it remains difficult to accurately describe the public-opinion propagation rules of social networks. In order to study the rules of public opinion spread on dynamic social networks, by analyzing the activity of social-network users and the regulatory role of relevant departments in the spread of public opinion, concepts of additional user and offline rates are introduced, and the direct immune-susceptible, contacted, infected, and refractory (DI-SCIR) public-opinion propagation model based on real-time online users is established. The interventional force of relevant departments, credibility of real information, and time of intervention are considered, and a public-opinion propagation control strategy based on direct immunity is proposed. The equilibrium point and the basic reproduction number of the model are theoretically analyzed to obtain boundary conditions for public-opinion propagation. Simulation results show that the new model can accurately reflect the propagation rules of public opinion. When the basic reproduction number is less than 1, public opinion will eventually disappear in the network. Social factors can significantly influence the time and scope of public opinion spread on social networks. By controlling social factors, relevant departments can analyze the rules of public opinion spread on social networks to suppress the propagate of negative public opinion and provide a powerful tool to ensure security and stability of society.

Keywords: public opinion propagation model direct immunization real-time online users basic reproduction number

Received: 06 June 2020
Revised: 13 July 2020
Accepted manuscript online: 28 July 2020

PACS:	02.60.Cb	(Numerical simulation; solution of equations)
	64.60.aq	(Networks)
	89.75.-k	(Complex systems)

Corresponding Authors: ^†Corresponding author. E-mail: guotianyi960531@gmail.com第一通讯作者 ^‡Corresponding author. E-mail: li@djtu.edu.cn

About author:

†Corresponding author. E-mail: guotianyi960531@gmail.com

‡Corresponding author. E-mail: li@djtu.edu.cn

* Project supported by the National Natural Science Foundation of China (Grant No. 61471080), the Equipment Development Department Research Foundation of China (Grant No. 61400010303), the Natural Science Research Project of Liaoning Education Department of China (Grant Nos. JDL2019019 and JDL2020002), the Surface Project for Natural Science Foundation in Guangdong Province of China (Grant No. 2019A1515011164), and the Science and Technology Plan Project in Zhanjiang, China (Grant No. 2018A06001).

Cite this article:

Yun-Ming Wang(王运明), Tian-Yi Guo(郭天一)†, Wei-Dong Li(李卫东)‡, and Bo Chen(陈波) Direct immune-SCIR public-opinion propagation model based on real-time online users 2020 Chin. Phys. B 29 100204

Fig. 1.

SCIR public-opinion propagation mode.

Fig. 2.

DI-SCIR public-opinion propagation model based on real-time online users.

Fig. 3.

Direct immunity probability P_SR changes with the interventional force α, and the real information credibility β. When α = 0.2859 and ʲ = 1, P_SR takes a maximum value of 0.7620.

Fig. 4.

BA scale-free network topology. Nodes having higher degrees are darker in color and larger in area, and the relationship between the nodes is indicated by a solid gray line.

Fig. 5.

BA scale-free network degree distribution logarithmic coordinate graph. The x-axis, k, represents the degree of nodes in the network, and the y-axis, P(k), represents the distribution of correspondence degrees.

Table 1.

Characteristic parameters of BA scale-free network.

Fig. 6.

Effect of basic reproduction numbers on the spread of public opinion: (a) random chosen R₀ = 1.3914 > 1; (b) random chosen R₀ = 0.9635 < 1.

Fig. 7.

Impact of interventional force α on the propagation of public opinion. α takes 0,0.1,0.3,0.55,0.7: density changes of (a) S state node, (b) C state node, (c) I state node, and (d) R state node.

Fig. 8.

Impact of real information credibility β on the propagation of public opinion β takes 0,0.1,0.3,0.5,0.7, respectively: density changes of (a) S state node, (b) C state node, (c) I state node, and (d) R state node.

Fig. 9.

Impact of interventional time T on the propagation of public opinion. T takes 3, 10, 20, 30, 40, respectively: density changes (a) of S state node, (b) C state node, (c) I state node, and (d) R state node.

Fig. 10.

Impact of different models on the spread of public opinion. The models are SIR, SCIR, and the DI-SCIR public-opinion propagation models based on real-time online users: (a) density changes of(a) of S state node, (b) C state node, (c) I state node, and (d) R state node.

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Direct immune-SCIR public-opinion propagation model based on real-time online users

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