Research On Multi-message And Multi-meme Spreading Dynamics On Online Social Networks

Online social networks(OSM)have become the current mainstream information dissemination medium.Research on the dissemination of information on OSM is of great significance for understanding the laws of dissemination and realizing effective control.On the one hand,revealing the mechanism and influencing factors of information dissemination helps people obtain useful information in a timely and efficient manner.On the other hand,understanding the dissemination mechanism of rumors and false messages helps to quickly control public opinion.There are still drawbacks in previous studies: 1)The research on message dissemination focuses on the daily message dissemination,but on emergencies is rare.2)Most research on network memes only pays attention to the influence of the characteristics of the living environment on the propagation of the meme,but not the influence of the characteristics of itself on its spreading dynamics.Among them,meme refers to the message that is copied through human imitation ability and spread in interpersonal relationship.In response to the above two issues,this thesis conducts the following research on the message dissemination in OSM based on complex network theory and message dissemination dynamics:First,this thesis conduct a statistical analysis of the rumor-defying data on the Weibo platform by "@Weibo rumor-defying",which in the emergency of the COVID-19 epidemic situation.The analysis results show that certain rumor-refuting messages with the same meme will appear frequently and have a low forwarding probability,which means that the corresponding fake messages also have a high generation rate and low forwarding probability.This thesis improves the true and false multi-message spreading model under emergencies,and introduces high-frequency false messages with high generation rate and low forwarding probability.The model simulates the propagation behavior of true and false multi-message competition propagation,especially the propagation behavior of high-frequency false messages.By simulating the model,this thesis verifies the propagation mechanism of competition among multiple messages,and shows that the probability of message generation and the probability of message forwarding are important factors that affects the competition among various types of messages.Secondly,this thesis conducts research on "network memes" on OSM.By collecting posts published by randomly sampled Internet users,according to the content of the post,it is classified into different memes,and then the similarity calculation is performed.It is found that the meme of posts posted by users are similar and positively correlated with their posting probability.This shows that users have a tendency to disseminate messages based on users’ memory interests.In view of this,this thesis proposes a multi-meme spreading model based on users’ memory interests.The forwarding probability of the meme in this model depends on the similarity between the meme and the user’s memory interest.The simulation results show that the model can better reproduce the large heterogeneity of life,popularity of real memes and user activity,and they conform to the power law distribution.This shows to a certain extent that the propagation mechanism of the model conforms to the real multi-meme propagation process.Finally,through a quantitative analysis of the user’s attention span in the model,this thesis reveals users’ preference for the spread of memes,that is,some users are more focused on memes,while some users are more distracted.This thesis studies the key factors that affect the propagation of memes,and adjusts the three parameters of user concentration,user forwarding activity and window length in the simulation process.The simulation results show that these parameters can affect the spreading life and popularity of memes to varying degrees.