A Network Public Opinion Trend Estimation Model Using a Scale-Free Network Algorithm

Abstract

Online public opinion is gradually becoming an important medium that influences society's mainstream direction in today's period of rapid development of new media and network oversight. The propagation of public opinion on the Internet has become a serious issue affecting economic and social development, as well as national security. As a result, this research investigates the evolution of public opinion and the evolutionary stability strategy of groups. Furthermore, a scale-free network is introduced to develop a network public opinion propagation model based on the scale-free network to tackle the problem that the existing public opinion propagation model cannot fully reflect the law of public opinion propagation in real life. The model incorporates components such as external environmental influences, individual personality characteristics, and interpersonal intimacy, in addition to the law of public opinion dissemination in real society. This study uses MATLAB to carry out simulation and comparison analysis in order to test the model's performance. Simulation findings reveal that this model's stability is around 95.6 percent, and its highest accuracy is 94.3 percent, which is greater than that of the Krause-Hegselmann and Deffuant models. The obtained forms of public opinion are richer, and the results of public opinion evolution are more in line with the process of public opinion evolution in real society, thanks to this model's ability to visually reflect the changes in individual opinions held over time in the process of public opinion evolution. It has practical implications for future studies.