MicroBlog User Ranking Research Based On Hadoop

Nowadays, the social networking platforms are becoming so indispensable to people. Microblog, one of the most popular social networking platforms, plays an increasingly important role on information spreading and user communication. The user influence ranking is one of the most important indexes of user, and it is the basis of user relationship. The greater the user influence, the greater the effect on information spreading. By analyzing the user basic information data and user behavior data with data mining, we can get the user influence ranking. It can not only provide technical support and solutions for the microblogging platform, but also make a profit in cooperation with advertisement owners.In the IT field, enterprises, media and technical personnel, are all talking about "big data". From the point of technology, Hadoop is one of the most important symbols of big data. Hadoop is a distributed computing platform which users can easily set up and use. Users can develop and run big data process program on Hadoop.This dissertation discusses the Hadoop platform and its related technology, and investigates the classical microblog user influence assessment algorithms, such as followers ranking and PageRank. Based on the classical microblog user ranking methods, this dissertation proposes an improved user ranking algorithm named UserRank. The UserRank algorithm is based on the classical PageRank and MapReduce programming model, which processes massive datasets with a cluster of machines on Hadoop platform. The UserRank takes the number and the quality of followers, the forwarding rate, the comment rate and whether the user is verified into consideration, and it uses comprehensive analysis to get the microblog user ranking. The UserRank algorithm runs on the Hadoop platform, and the experiment and evaluation result testifies that the UserRank algorithm can achieve a more accurate and effective ranking result.