An Improved User Identification Method Across Social Networks Via Tagging Behaviors

User Identification problem is concerned with identifying the same person with multiple virtual identities across social network sites(SNSs). Most of the existing approaches pays close attention to the similarity of profile attributes, generate-contents and linkages of friends or simply combination of these features. Only one method analyzes the feasibility of user tags in User Identification problems, but does not analyze the particularity and the inconsistency of tags belong to users among different social networks. In this paper, an improved user identification method across social networks via tagging behaviors is proposed that a new symmetric variant of BM25 (BM25 is a bag-of-words retrieval function that ranks a set of documents based on the query terms appearing in each document, regardless of the inter-relationship between the query terms within a document) using the semantic relationships between inconsistent tags among different social networks. By using extracted features from the inconsistent tagging behaviors, profile attributes and SVM supervised learning techniques, a classifier is developed for performing user identity matching between two social network sites. Evaluation on Douban and Weibo real world data-set showed that the accuracy of the proposed method is 30% higher than that of the common tag-based approach.