Research On Mining Key Users In Microblog Network Under Spark Environment

With the rapid development of mobile Internet technology,the functions and attributes of social networking platforms have become more and more abundant,and the user's stickiness has increased.The number of active daily users of social platforms represented by WeChat and Microblog is Reaching the scale of hundreds of millions,and they are continuously increasing.The key users are the topic authority,network core,and focus of attention in the social networking community.Compared with the general users,the social users have a larger range of social radiation,stronger indirect influence,and better network transmission.Extracting representative key users from social networks and analyzing and researching them can have important application value in hotspot event prediction,public opinion monitoring,precision marketing,and personalized recommendation.In order to solve the problems of incomplete evaluation results and low mining efficiency in the current key user mining of social networks,this thesis takes the Microblog platform as the research object,and proposes a multi-dimensional comprehensive effect based on online community mining in the Spark environment.Force evaluation model combined with key user mining methods.The main research content is:1.Through the integration of Microblog network topology structure and user multi-dimensional feature information,the overall scheme of key users mining on Microblog network is given.That is,the key user evaluation scope is first defined by the user community,and then the key degree of the users in the community is measured by the user comprehensive influence evaluation model.The mining scheme is built under the Spark environment,which not only ensures the accuracy of the identification of key users of Microblog,but also can cope with large-scale and complex social network scenarios.2.In the mining process of Microblog users' communities,based on the relationship of user links,the degree of relevance of users' interests is described by introducing the similarity of user blog topics,so as to increase the interest cohesion of the community.The similarity is mapped as the weight of the link relationship between the Microblog users,and the Louvain algorithm is used to process the efficiency advantage of the large-scale complex network to conduct community mining.Experiments show that the community discovered by this method has the characteristics of thematic concentration and high degree of community cohesion,and the algorithm runs efficiently.3.In the evaluation of Microblog user influence,we studied and analyzed the feature information of Microblog users in multiple dimensions,and proceeded from the four evaluation indicators of user's authority,dissemination,interactivity,and linkability,and proposed micro Bo users comprehensive influence evaluation model.Experiments show that compared with the existing user influence evaluation methods,this model expands the measurement metrics of user influence,making the measurement result more accurate and comprehensive,and can reflect the user's potential influence while measuring the user's potential influence.