Research On Information Spreading In Online Social Networks

As human society enters a highly informative era,the sources and channels for people to obtain information have become vast and complicated.From the original word-of-mouth to today’s short videos and hot searches,people can forward and spread the news they know in just a few seconds.The emerging factors such as hashtags and social behaviors that accompany modern social networks also make it difficult to adapt and expand the traditional model.Therefore,it has become an important problem to investigate the pattern of information dissemination in online social networks based on the emerging factors,to achieve accurate prediction of the scale and trend of dissemination,and to accomplish the promotion or suppression of different messages.In this thesis,the main work to investigate the law of information dissemination in online social networks is as follows.(1)Based on the actual information propagation rules in modern online social networks,a propagation dynamics model driven by topic tags and social behaviors is established.The model distinguishes information forwarding into two types: topic forwarding and relationship forwarding.Treating social behavior as a higher-order interaction,the process of topic groups being subjected to social behavior is simulated by the social behavior model.A network evolution model is constructed based on the centrality and non-continuity characteristics of topic groups in the process of communication.By defining topic expansion rate and topic diffusion rate,the social reinforcement effect in information dissemination is described from two dimensions.The experimental results show that the information dissemination model driven by topic labels and social behaviors has better dissemination effects and can effectively adapt to the real online social network environment.(2)A model of information dissemination dynamics based on the control role of announcements is established,focusing on the influence of the control role of announcements on the dissemination process in social networks.In this part,the time point of verification intervention is used as the boundary,and two propagation stages are distinguished: the unverified propagation stage and the verified propagation stage.Based on the actual propagation rules in online social networks,two validation outcomes are defined: true information or false information.The changes in the propagation effects due to different verification results are analyzed by building a twostage information propagation dynamics model based on announcement control.The influence of intervention time on the overall dissemination process is analyzed by combining important control factors such as response cost and announcement timeliness.It is demonstrated that fast and efficient information disclosure can effectively improve the efficiency of communication and the satisfaction of information audiences.This work has been analyzed and visualized from multiple perspectives,and the findings provide a scientific basis for the optimal intervention time of information control and a theoretical foundation for further improvements in the fields of governmental disclosure and public services.(3)A spread dynamics model based on the higher-order structure of social networks is established to deeply analyze the higher-order interactions in the spread process.This part interprets the long-linked edge structure in traditional research as a higherorder information dissemination pathway,rather than a simple change of the linked edge structure.It avoids the homogenization of information level and redundancy of network structure,and establishes a propagation bridge model based on the higherorder long-linked-edge structure accordingly.At the same time,the length scale of the propagation bridge structure is analyzed and the bridge propagation probability is established.The propagation dynamics model with multiple interactions is developed by combining the traditional binary interactions of direct-connected propagation with bridge-connected propagation.Finally,the validity of the model is verified by comparing the model simulation results with real cases.