Research And Application Of Rumor Blocking Algorithms In Social Networks

With the increasing popularity of electronic devices and the convenience of information diffusion,social networks provide an efficient medium for the propagation of various negative information.Rumors are one of the prominent forms of negative information on social media,which can trigger social unrest and cause economic losses.Therefore,how to quickly find a group of users with positive influence to block the spread of rumors to the greatest extent is the problem of rumor blocking,and it is also a hot spot in the current social network research field.Although a lot of work on rumor blocking has been accumulated so far,there are still the following limitations:some work does not consider cost constraints,and the blocking efficiency of some work needs to be further improved.Therefore,in order to facilitate the relevant regulatory authorities to obtain,monitor,analyze and block the spread of rumors in a timely manner,this paper studies how to block the spread of rumors from two perspectives.One is to study the rumor blocking problem based on community detection,which further improves the blocking efficiency,considering the community aggregation of rumor spreading;the other is to study the rumor blocking problem under cost control,considering the cost of rumor blocking.Meanwhile,a corresponding blocking analysis system is developed based on the proposed two blocking algorithms.The main work and research results of this paper are as follows:(1)An algorithm of maximizing rumor blocking based on community detection,CDRBM,is proposed under the CIC(Competitive Independent Cascade)model.The algorithm first performs community detection,and then allocates positive seed node budget for each community by estimating the scope of rumor infection in each community.Then,considering the network topology and rumor throughput,an index of node strength,which is a measure of node importance,is proposed and used to select positive seeds.Finally,through experiments on real-world datasets,the results show that compared with other algorithms,the proposed algorithm in this chapter is more effective in rumor blocking within acceptable running time.(2)A rumor blocking maximization algorithm,RBM-CC,is proposed based on two-stage strategy under CIC model.In the first stage of the algorithm,the candidate node pool with the largest coverage is obtained by sampling;then in the second stage,under the premise of considering the node blocking cost,the algorithm selects the positive seed set with the best blocking effect for Minimize the spread of rumors.Through experiments on real-world datasets,the results show that the algorithm proposed in this chapter is significantly better than other algorithms.(3)Based on the algorithms proposed above,we design and implement a rumor blocking analysis system.The system can provide users with functions such as uploading and downloading of network data sets,public opinion push,visualization the results of rumor blocking maximization,and user management.After testing,the system has a friendly interface and stable operation.It can conveniently and efficiently analyze the spread of rumors.It has important social value and promotion significance in the detection of public opinion and the handling of emergencies.