Abstract
With the popularization of the Internet and the arrival of the big data era, numerous different social networks (SNs) have emerged to satisfy users’ social needs and offer them rich content and convenient services. Under these circumstances, identifying multiple social accounts belonging to the same user across different SNs is of great importance for many applications. Across social networks user identification (ASNUI) can help perfect user information, offer personalized service recommendation, and data mining, as well as provide support for scientific research. This paper first systematically introduces the application of ASNUI in the field of social computing, then states its applications and challenges, and reviews the adopted models, frameworks, and performance comparison state-of-the-art techniques used in ASNUI. Finally, we also identify a few future research directions in ASNUI, such as weight allocation of user attribute information, the fusion of multi-dimensional information, and large-scale user identification.
Highlights
We are currently experiencing an explosive growth in the amount of internet data
We provide three general and formal models for Across social networks user identification (ASNUI) according to different user data, and give a unified identification framework; 3) User identification mainly consists of two aspects: similarity calculation and account matching
User identification based on user behavior information analyze the content published by users on SNs, compare the similarity of behavior information between different social accounts to determine whether the user identities match or not
Summary
LING XING 1, KAIKAI DENG 1, HONGHAI WU 1, PING XIE1, H. VICKY ZHAO 2, AND FEIFEI GAO 2, (Senior Member, IEEE).
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