DR-SCIR Public Opinion Propagation Model with Direct Immunity and Social Reinforcement Effect

Abstract

The DR-SCIR network public opinion propagation model was employed to study the characters of S-state users stopping transmitting information for the first time and secondary transmission of immune users. The model takes into account symmetry and complexity such as direct immunization and social reinforcement effect, proposes the probability of direct immunity Psr and the probability of transform from the immune state to the hesitant state Prc, and divides public opinion information into positive public opinion and negative public opinion based on whether the public opinion information is confirmed. Simulation results show that, when direct immunity Psr = 0.5, the density of I-state nodes in the model decreased by 54.12% at the peak index; when the positive social reinforcement effect factor b = 10, the density of I-state nodes in the model increased by 16.67% at the peak index; and when the negative social reinforcement effect factor b = -10, the density of I-state nodes in the model decreased by 55.36% at the peak index. It shows that increasing the positive social reinforcement effect factor b can promote the spread of positive public opinion, reducing the negative social reinforcement effect factor b can control the spread of negative public opinion, and direct immunization can effectively suppress the spread of public opinion. This model can help us better analyze the rules of public opinion on social networks, so as to maintain a healthy and harmonious network and social environment.

Control Mechanism of Virus Propagation in Wireless Sensor Networks Based on Analytic Hierarchy Process

“…e uniform control method randomly [19][20][21] selects a certain proportion of nodes as immune nodes in the network. is mechanism is relatively simple to apply, but the accuracy is not high, the immune cost is high, the virus suppression effect is not apparent, and the virus spread cannot be accurately and quickly suppressed due to the strong randomness.…”

Effect of Global and Local Refutation Mechanism on Rumor Propagation in Heterogeneous Network

“…Li et al considered the influence of government punishment on SIS and SIR rumor propagation models [29]. Li et al proposed a model considering the symmetry and complexity of the social strengthening effect [30]. In terms of the local influence of the spread of rumors, Wang et al studied the influence of differences in network individual identification capabilities on rumor propagation [31].…”

Model of Negative Emotional Information Communication among Netizens under Corporate Negative Events

“…Moreover, many researchers investigated the process of emotion propagation based on the SIR (Susceptible-Infected-Removed) model [17][18][19]. Liu et al constructed an SEIR (Susceptible-exposed-Infected-Removed) model of rumor propagation and simulated the propagation dynamics of rumors in microblogs [17].…”

“…Liu et al constructed an SEIR (Susceptible-exposed-Infected-Removed) model of rumor propagation and simulated the propagation dynamics of rumors in microblogs [17]. Li et al identified how to direct immunization and social reinforcement effect on positive or negative public opinion through the altered SIR model and dynamic simulation [19]. In a word, current research on emotional communication models has achieved considerable results, which provide theoretical references in the evolution of emotional information and negative opinion guidance [20,21].…”