An Analysis Of Tencent Microblogging User Behavior From The Perspective Of Large Data

The SNS are becoming more and more popular in recent years. Especially after the Sina Weibo was online in 2009, China entered a new "Weibo Era". Since the SNS users record their ideas online anytime and anywhere as they want, it brings the Internet a large amount of "User-Generate Content (UGC)". These contents contained invaluable information and very huge opportunities. How to find something useful from these data is what we care about.By using a web crawler development by ourselves to get the twitter information by Tencent microblog users, to analysis and explain the data from a big data view, we got that the using behavior of the users is a pattern. And by classify the different kinds of the users, we found out some user groups, and analyzed the characteristics of different groups.At the third part of the paper we mainly introduced the data source, the specific content of the data. Then after the initial procession, we analyzed the data integrally. Our analysation includes the distribution of the microblog time in one month one week scope, and in a whole day time. What we found is that the using time of Tencent microblog is more frequent on work day than on weekends. And most of the microblog were twittered around 9 a.m. and 10 p.m.Classifying the users by different regular pattern of their microblogging time, we analyzed the characteristics of different user groups. First, we use the k-means algorithm to cluster the users to some different groups. Then find out the characters of every different group, and choose some of them as representative. The results show that it indeed has regulation in the using habit of the Tencent microblog users, and this may has relation with their real life.