Public Opinion Guidance Model in Major Public Crisis Events Based on Accelerated Genetic Algorithm
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The research aims to enhance the effectiveness of the public opinion evolution guidance model, with a particular focus on the influence of opinion leaders, and address the shortcomings of traditional models, such as neglecting opinion leaders and insufficient network topology. Therefore, the relevant scale-free network is used to improve the traditional public opinion evolution model. An improved model integrating the two is proposed by introducing a real coded accelerated genetic algorithm. The experimental results show that the proposed model converges to four opinion clusters, with the average values of negative and positive opinions being 0.399 and 0.370, respectively, demonstrating the trend closest to the actual data. When the parameters are fixed, the ultimate development of public opinion shows obvious changing trends in different situations, and the validity of the model has been proved by practice. The research innovatively introduces the scale-free network based on Barabasi and Albert, and improves the Hegselmann Krause model. Meanwhile, by comprehensively considering the influence of opinion leaders and network topology factors, the model overcomes the shortcomings of traditional models in public opinion guidance and also demonstrates good practicability in practical applications.
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