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A Multiple Salient Features-Based User Identification across Social Media
Authors:Yating Qu  Huahong Ma  Honghai Wu  Kun Zhang  Kaikai Deng
Institution:1.School of Automotive and Rail Transportation, Luoyang Polytechnic, Luoyang 471099, China; (Y.Q.); (K.Z.);2.School of Information Engineering, Henan University of Science and Technology, Luoyang 471023, China;3.School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing 100876, China;
Abstract:Identifying users across social media has practical applications in many research areas, such as user behavior prediction, commercial recommendation systems, and information retrieval. In this paper, we propose a multiple salient features-based user identification across social media (MSF-UI), which extracts and fuses the rich redundant features contained in user display name, network topology, and published content. According to the differences between users’ different features, a multi-module calculation method is used to obtain the similarity between various redundant features. Finally, the bidirectional stable marriage matching algorithm is used for user identification across social media. Experimental results show that: (1) Compared with single-attribute features, the multi-dimensional information generated by users is integrated to optimize the universality of user identification; (2) Compared with baseline methods such as ranking-based cross-matching (RCM) and random forest confirmation algorithm based on stable marriage matching (RFCA-SMM), this method can effectively improve precision rate, recall rate, and comprehensive evaluation index (F1).
Keywords:across social media  user identification  salient features  similarity  bidirectional stable marriage matching
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