Research On Multiple Information Diffusion On Online Social Networks

With the rapid development of information technology,online social platform has become an important way for people to communicate with others and get information.On the one hand,the real-time performance of online social platform facilitates people on opinion expression and information exchange.On the other hand,the openness of online social platform also makes false information have the ability to diffuse.The rapid outbreak of COVID-19 in 2020 led in the spread of false information,and people gradually realized that the spread of information would have an important impact on epidemic prevention and social stability.Now,most existing studies focus on the empirical analysis or theoretical researches of single information such as news or rumors,and few of them learned on the empirical data of online social networks.Based on the methods and theories of complex networks,this thesis studies the multiple information propagation on online social networks as followings:Firstly,this thesis crawls the rumor refutation data of Weibo platform and the epidemic data of China Health Commission,and then compares and analyzes the time delay correlation between the number of rumor refutation and the number of epidemic.We find that the evolution of epidemic will drive the spread of information and lead to the difference of the distribution of dissemination popularity in different stages.Based on the empirical data analysis,we build an information diffusion model driven by emergencies,and verify that the evolution of event and information competition are the important factors that affect the distribution of information dissemination popularity,and the numerical simulation results are consistent with the empirical phenomena.Secondly,this thesis obtains the propagation data of true and false information on the Twitter,and constructs the forwarding cascade network of each message through the forwarding relationship in the data set.By comparing and analyzing a large number of empirical data,we find that false information has obvious spreading advantages,mainly reflected in the dissemination popularity and structural virality.Then,we analyze the difference of the annual generation and forwarding between true information and false information through the propagation time in the data set,and find that both the generation and forwarding probability of false information are significantly higher.At the same time,this thesis studies the topology characteristics of the forwarding network of true and false information,and finds that the forwarding process of true information is more inclined to broadcast propagation,while the forwarding process of false information is more inclined to tree propagation.This shows that the generation probability of information,the forwarding probability of information and the direction of information propagation will jointly affect the ability of information propagation.Finally,based on the analysis results of empirical data,this thesis constructs a true and false multiple information propagation model with limited attention of users,and explores the influence of information generation probability,forwarding probability and trust probability on the dissemination popularity and structural virality.In the process of communication,many different true and false information will be subject to the limited attention of users and produce competitive effects.Based on a large number of simulation experiments,we verify that the probability of information generation,forwarding probability and trust probability can affect the distribution of dissemination popularity and structural virality of true and false information.The simulation results can be consistent with the empirical results.The empirical analysis and model construction on online social networks will help people understand the law and mechanism of information diffusion,and provide important guides for public opinion control and emergency warning on online social networks.