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.
Highlights
In recent years, with the rapid development of communication technology, the way of socializing has gradually shifted to online platforms, resulting in a large amount of online public opinion information
The DR-SCIR network with a positive social reinforcement effect promotes the spread of positive public opinion that increases the number of users in the hesitant state C, while the DR-SCIR model with a negative social reinforcement effect suppresses the propagation of negative public opinion
After considering the relevant department’s supervision of the spread of public opinion that makes the network have a direct immune effect and the impact of social reinforcement effects on immune users comprehensively, we propose the DR-SCIR network public opinion propagation model with direct immunity and social reinforcement effects based on the SCIR model
Summary
With the rapid development of communication technology, the way of socializing has gradually shifted to online platforms, resulting in a large amount of online public opinion information. The simulation results showed that the model is more in line with the propagation characteristics of real online social networks, and the important acquaintance immune strategies can effectively solve the problem of online social networks rumor suppression problem [25]. Cohen studied the nature of acquaintance control strategies based on traditional rumor propagation models, proposed a control strategy that is broad, especially effective for scale-free networks, and obtained the threshold of its complete immunity [26]. We propose the DR-SCIR public opinion propagation model with direct immune effect and social reinforcement effect based on the SCIR network model in this paper. After the number of times the user contacts the same public opinion information exceeds the threshold, the social reinforcement effect will act on the immune users.
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