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The identification and association of multiple identities in different online social networks (osns) is an important problem, and also is the basis for many applications. At present, most of technologies try to solve this problem by matching the username of social networks or calculating the similarity of a pair of users’ personal information from different platforms. However, due to the anonymity of social networks, these methods often fail to identify and associate multiple virtual identities. In this paper, we propose a classification method based on machine learning. Our method jointly consider the time, the text and the topic of the similarity to construct the feature vector to characterize the user’s relationship. And we use the feature vectors to train the classifier. The model is evaluated on real world dataset, the twitter and sina weibo. The experimental results show that our method is effective. Introduction Today,the online social networks have penetrated into all aspects of daily life, and also have played an important role in the impact of social politics, economy, culture and other fields of the industry. Different social networking platforms offer a variety of web applications [1], such as the exchange of information, games, chat and other services. It is a common phenomenon that a user has more than one account on different platforms. This phenomenon, which brings convenience to users, at the same time, also causes the information fragmentation and becomes the bottleneck of users’ behavior analysis on social networks. It has important implications to associate with accounts on different social networks, such as open intelligence collection, find specific groups, specific information analysis and information dissemination or product recommendati-on. Maybe it is an important evidence that the user’s unique ip address or mac address to solve this problem. Unfortunately, the ip address and mac address in these platforms belong to the user’s privacy information, it is difficult for us to obtain such information. Some methods are proposed by matching username or user’s information. However, usernames in social networks are often anonymous and users’ information is often error or unreal, as a result, which causes these methods cannot solve the problem effectively. So it is needed to propose another more effective method to connect multiple virtual identities from a real person. In this paper, we calculate the number of the posts from two users at each time interval and their similarity. Then, we get the similarity of two users’ posts by thetf-idf 137 Advances in Computer Science Research (ACRS), volume 54 International Conference on Computer Networks and Communication Technology (CNCT2016)

Users Connection across Social Media Sites Based On Users'Relationship Vector