Analysis and Research of network public opinion propagation model GSN-SEIRO

With the popularity of social media, the transmission of network information becomes more and more convenient, which makes the scale and complexity of network public opinion increase continuously. However, some existing Netlogo models about the spread of network rumors may oversimplify the complexity of the spread of network rumors, ignoring the social network structure and other factors of people in reality. This can lead to limitations in the predictive power and applicability of the model. By improving the traditional SEIR virus propagation model and JGN growth social network model, the improved GSN-SEIRO network public opinion propagation model is proposed. The GSN-SEIRO model combines the social network model and takes into account the forgetting situation of people in reality, which increases the forgetting rate in the model, making the rumor propagation closer to real life. The model starts from two aspects of the network public opinion propagation path and the social structure of the crowd in reality, simulates the process of network public opinion propagation in the Netlogo simulation platform, and analyzes the simulation data. Through the simulation experiment, it is found that the rumor propagation trend accords with the real public opinion propagation situation. Data analysis shows that subject forgetting rate, frequency of public opinion monitoring and recovery rate have a positive impact on the spread of Internet rumors, while rumor spreading probability and crowd interaction frequency have a negative impact on the spread of Internet rumors.