Abstract: With the development of society,there has been a more and more obvious presence of the characteristic of audience-segmentation in human activity over information spreading,and user classification has also become an important research topic.So the article carried out a study over online social network user from multiple perspectives which mainly include user classification based on interested topics and preference,classify interests of social media user based on information content and topological relation,and both them respectively.For user classification based on information content,we adopted LDA to extract the topic distribution from the content posted by users.And the distribution is used in support vector machine,decision tree,Bayes and other multiple models to classify interests of users.For user classification based on topological relation,we found that users with same interests tend to have more common fans,and based on this finding we built classification models to classify users.Then,we proposed methods of combining information content and topological relation to classify users.Based on the experiments using Twitter data,we found that the combined method outperforms the one based on information content or topological relation.

Key words: Online social networks,User classification,LDA,Topological relation