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

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.

AI-Based Secure Undergraduates’ Ideological and Political Public Opinion Strategy

“…In [18], the media-and interpersonal-based SEIR model was proposed to represent the real propagation of cyber opinion. In [19], the direct immune-susceptible, contacted, infected, and refractory public-opinion propagation model based on real-time online users was established. In [20], the authors proposed a novel block chain-based social network public opinion propagation model to stop the propagation of false public opinion.…”

“…and Political Public Opinion. In order to verify the effectiveness of the proposed propagation model, three baselines are chosen for comparison, which are SN-SEIR [17], MI-SEIR [18], and DI-SCIR [19]. SN-SEIR is a Susceptible-Exposed-Infectious-Recovered (SEIR)-based public opinion propagation model on social networks MI-SEIR is an SEIR-based media and interpersonal relationship propagation model, and DI-SCIR is a Direct Immune-Susceptible-Contacted-Infected-Refractory public opinion propagation model.…”

A Multilingual SEIR Model for Public Opinion Dynamics: Integrating Social Enhancement and Cross-Transmission Mechanisms

“…Zanette [9][10] , a pioneer in the study of rumor propagation within complex networks, highlighted a propagation threshold in small-world networks and the significant influence of network structures on this process.The diverse topologies of complex networks have spurred significant interest and yielded substantial findings in rumor propagation dynamics. This includes models for information propagation across various complex networks such as random, small-world, homogeneous, heterogeneous, large-scale scale-free networks, those with adjustable clustering coefficients, time-varying, and multilayer networks [11][12][13][14][15][16][17][18][19][20][21][22][23][24] .…”

Correlation and trust mechanism-based rumor propagation model in complex social networks

“…The bulk of rumor diffusion models have been investigated on the basis of epidemic spreading models because of many similar features between the two forms of transmission. [5,6] A portion of scholars tried their best to extend the standard SIR model [7] to other complicated models, including the SEIR diffusion model, [8] DI-SCIR model, [9] SHIR mode, [10] ISCR rumor spreading model, [11] etc. Li et al [12] considered that the node of the Hibernator coming from the spreaders due to forgetting factors and later becoming spreaders again was introduced in the diffusion model.…”