Research On Micro-blogging Community-finding

As the most fashion internet application, Micro-blogging attracts cyber citizen. In the report from sina on Oct, 2010, there are over 50 million people who use sina Weibo. And the user of twitter is even more over 0.2 billion.The Micro-blogging fast increase leads to a new question: how can we find a companion from so many people, which we call community. We will find the answer to this question aim to some data mining knowledge. It is different from the tradition community-finding, because the micro-blogging has its own feature, which needs the algorithm must be more efficient and the model must be more complex. We will research Community-finding, build a model base on the Bayesian model comparison, and find a way to quantify the relationship between one user and one community. We also research on user-influence in the micro-blogging. We build the model from the data of user, quantify the influence of users. To check these algorithms, we do many experiments on the off-line data. We collect data from the sina Weibo through the web crawler, and obtain over 700,000 user data. After these experiments, we prove our algorithms are effectiveBesides this, we also try to finding the opinion leader in our study. We brings an algorithm about user influence, and the demo based on this theory show it's effective to find the opinion leader.