Analysis And Modeling Of Evolution Dissemination Mechanism Of Network Public Opinion In Big Data Network

With the development of the Internet and the coming of the era of big data,it is more convenient for people to get information.The convenience of the network makes people's access to information no longer restricted to the traditional media such as newspapers,radio and television,and information gradually develops from one-way transmission to two-way transmission.At the same time,the improvement of user usage of social platform makes people's communication more convenient,and the network has become the main position for people to express their views and views.Compared with the traditional media,the network has more open,richer and faster characteristics,which can allow public opinion to spread among the public,and to a large extent affect the direction of public opinion,bringing new risks and challenges to public opinion control in China.At the same time,with the increase of the number of Internet users in China,the amount of data generated by public opinion events has blowout growth,which has brought more advanced big data analysis technology.Integrating the research of network public opinion with big data is a new challenge in the field of public opinion research,which has important theoretical and practical significance.In this paper,the evolution and dissemination of network public opinion is modeled and analyzed under the background of large data.In terms of the evolution of network public opinion,the Deffuant model is improved by combining complex networks,and the weights of directed edges are added as interactive factors.Considering the presence of opinion leaders,the mechanism of the evolution process of network public opinion is analyzed.It is found that the increase of the average node degree in a group has little effect on the number of viewpoint clusters after evolution.Within a certain degree,it can accelerate the convergence speed of individual viewpoints,make the distribution of viewpoints more obvious in the group,and facilitate the unification of small-scale viewpoints.When the group has greater intimacy,the viewpoints are easier to achieve unification.At the same time,opinion leaders play a key role in the evolution of group views,which has a great impact on the speed of group convergence and the unity of views.If opinion Leaders'views are seriously divided,it will cause a large number of individual views to be biased.In the aspect of network public opinion dissemination,by combining BBV network model with triangular connection,the original network model has the characteristic of adjustable clustering coefficient.The classical SIR model of infectious diseases is introduced to determine the transition probability of three states in the network through the dynamic weight value of the edges between nodes.The dissemination process mechanism of network public opinion is analyzed.In the experiment,the SIR model of BBV network based on adjustable clustering coefficient has certain reliability.The distribution of node degree,point strength and edge weight satisfies power law distribution on the whole.By adjusting the probability of triangular connection,the clustering coefficient of the network model can be directly affected.Finally,taking Hongmao Drug Wine Reputation Damage Case as an example,using the public opinion evolution communication model proposed in this paper,the evolution and transmission of events are analyzed respectively,and the validity of the model is verified.This study can provide decision-making theoretical support and reference for public opinion analysis and control in China.