Research on public opinion guidance of converging media based on AHP and transmission dynamics

In the 5G era, media convergence and technological updates lead to tremendous changes in the dissemination of public opinion information. The guidance of public opinion in the converged media environment is a new research topic. Users participate in the discussion of different media, and flow in and out from different media, which gradually generate a new complex dynamics model of the dissemination of public opinion information. An index system for evaluating the influence of converged media communication that combines the advantages of traditional media and new media is constructed. We use AHP to determine the index weights in the evaluation system and conduct consistency tests. The final weight of each media is determined through the combination of subjective and objective methods, which provides data supports for later determining the participation of various media in the dissemination process of public opinion information. Based on the SIR model, a UCIR (unknown-contact-infected recovered) dynamic model is established. The simulation results show that the root mean square error (RMSE) of the UCIR model is 31.6% lower than that of the SIR model. Finally, by fixing the key parameters alpha, beta, theta, epsilon, p, q in the UCIR model, and changing only one of them, we studied the effect of the transition probability between different states on the process of public opinion information transmission, and then proposed corresponding guidance. In addition, from the two perspectives of network media and government, the countermeasures and suggestions for the guidance of public opinion are proposed. We try to solve the problems of humanities and social sciences with the method of natural science. These research results can provide theoretical and methodological support for people to understand the law of public opinion information transmission and establish a guiding mechanism for public opinion information transmission.