The Research Of Weibo User And Weibo Influence Ranking Based On Hadoop

With the advent of Web2.0 era, the user dominant factor in the Internet accounts for an increasingly important role. The social network platform which under the leadership of Weibo has obtained the unprecedented development. Weibo has become an important platform for information transfer and social public opinion. Therefore, studying the influence of Weibo users and Weibo has important research significance and commercial values.The study of this paper is divided into Weibo user influence and Weibo influence. For the assessment of the Weibo user influence, the People Rank algorithm which is based on the Page Rank algorithm model is widely used in the current. But the People Rank algorithm is just applied the page rank model to the ranking of Weibo user influence simply and the accuracy of the assessment of the user influence should be improved. By integrating the microblogging personal authentication feature of Sina Weibo, the paper proposes the NPRank(New people Rank) algorithm which is improved from the People Rank. For the assessment of the Weibo influence, at present, more commonly used method is based the number of microblogging forwarding and comments as the evaluation criteria. This method takes into account the index is not comprehensive enough, and the reliability of the assessment of the Weibo influence is need to be improved. This paper is considering the number of microblogging forwarding, comments and point like of cases, adding a factor of microblogging publisher‘s user influence as the Weibo influence assessment standard. And proposes a microblogging influence assessment method—TRank(Topic Rank) algorithm.For the feature of Weibo user influence ranking and Weibo influence ranking in need of massive data processing, this paper proved that the NPRank and the TRank algorithm running on Hadoop platform is feasible by the experiment, and launched a comparative analysis of the experimental results. It also verifies that the NPRank algorithm is better than the People algorithm in terms of accuracy, and the TRank algorithm has a strong reliability on the assessment of Weibo influence. Finally, based on the ranking results of NPRank ank TRank, the paper also proposes Weibo influence ranking display system design.