ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2016, Vol. 53 ›› Issue (11): 2630-2644.doi: 10.7544/issn1000-1239.2016.20150219

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A Method for Social Network User Identity Feature Recognition

Hu Kaixian1,2, Liang Ying1, Xu Hongbo1, Bi Xiaodi1,2, Zuo Yao1,2   

  1. 1(Key Laboratory of Network Data Science and Technology (Institute of Computing Technology, Chinese Academy of Sciences), Chinese Academy of Sciences, Beijing 100190); 2(University of Chinese Academy of Sciences, Beijing 100049)
  • Online:2016-11-01

Abstract: Social network is an important part of modern information society. The anonymity of social network users brings a series of problems concerning social security. This paper presents a method to recognize social network user identity feature by location-based social network (LBSN) and social relationships, and combine the results of those two to infer social network user true identity. The method of geo-location uses approximation weight which is calculated by computing full match weight and basic match weight based on Chinese segmentation and bi-word segmentation to evaluate the possibility that the entity is where the user studies or works, and the method uses entity name aggregation algorithm to optimize the result of approximation weight calculation. According to the observation that friend relationship between users on social network tends to indicate a certain same identity features or a share of common interests, the method of social relationships uses majority voting scheme to count users friends identity features to infer user address, entity information and interests. Based on microblog data, we conduct experiments on two samples which cover 1 000 users and 10 000 users respectively and involve a total of more than 2.5 million users relationships. Results shows that our method has a high rate of precision and recall. Compared with the existing methods, our method focuses on individual user identity feature and is valuable in practice.

Key words: identity recognition, user identity features, location-based social network (LBSN), social relationships, de-anonymizing

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