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In the dissemination of information on the Internet, internet water armies have gradually become an important influencing factor in the direction of public opinion. This paper takes Weibo as the research background, obtains real data, establishes a communication dynamics model, and quantitatively researches the influence of internet water armies on the dissemination of public opinion in social networks. Firstly, this paper obtains real hot events in Weibo, analyzes the number of retweets and comments under the same hot topics, and finds the users of the internet water armies. Secondly, this paper establishes a new social network SIwIcS propagation model based on the propagation mechanism among users in Weibo, combining with the SIS infectious disease model, and introducing the impulse effect, the heat effect, and the herd effect, to realize the research of public opinion propagation in Weibo. After that, the SIwIcS propagation model is applied to small-world and scale-free networks to simulate the propagation of public opinion in social networks and analyze the influence of internet water armies on the propagation. The experimental results summarize the influence of network water armies on the propagation of public opinion. Finally, the acquisition of real data is utilized to verify the rationality and validity of the model. This paper quantitatively considers the driving nature of internet water armies on the trend of public opinion in the communication dynamics model, which provides a reasonable decision-making basis for the dissemination and control of public opinion in social networks, and has important theoretical and practical significance.
In the dissemination of information on the Internet, internet water armies have gradually become an important influencing factor in the direction of public opinion. This paper takes Weibo as the research background, obtains real data, establishes a communication dynamics model, and quantitatively researches the influence of internet water armies on the dissemination of public opinion in social networks. Firstly, this paper obtains real hot events in Weibo, analyzes the number of retweets and comments under the same hot topics, and finds the users of the internet water armies. Secondly, this paper establishes a new social network SIwIcS propagation model based on the propagation mechanism among users in Weibo, combining with the SIS infectious disease model, and introducing the impulse effect, the heat effect, and the herd effect, to realize the research of public opinion propagation in Weibo. After that, the SIwIcS propagation model is applied to small-world and scale-free networks to simulate the propagation of public opinion in social networks and analyze the influence of internet water armies on the propagation. The experimental results summarize the influence of network water armies on the propagation of public opinion. Finally, the acquisition of real data is utilized to verify the rationality and validity of the model. This paper quantitatively considers the driving nature of internet water armies on the trend of public opinion in the communication dynamics model, which provides a reasonable decision-making basis for the dissemination and control of public opinion in social networks, and has important theoretical and practical significance.
No abstract
The complexity of systems stems from the richness of the group interactions among their units. Classical networks exhibit identified limits in the study of complex systems, where links connect pairs of nodes, inability to comprehensively describe higher-order interactions in networks. Higher-order networks can enhance modeling capacities of group interaction networks and help understand and predict network dynamical behavior. This paper constructs a social hypernetwork with a group structure by analyzing a community overlapping structure and a network iterative relationship, and the overlapping relationship between communities is logically separated. Considering the different group behavior pattern and attention focus, we defined the group cognitive disparity, group credibility, group cohesion index, hyperedge strength to study the relationship between information dissemination and network evolution. This study shows that groups can alter the connected network through information propagation, and users in social networks tend to form highly connected groups or communities in information dissemination. Propagation networks with high clustering coefficients promote the fractal information dissemination, which in itself drives the fractal evolution of groups within the network. This study emphasizes the significant role of “key groups” with overlapping structures among communities in group network propagation. Real cases provide evidence for the clustering phenomenon and fractal evolution of networks.
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