Investigation of Cross-Social Network User Identification

Abstract

The development and popularization of Internet technology has stimulated the growth of users' network demands. A large number of users will choose many different social networks to provide users with rich content and services. Cross-social network user identification can help improve user information, provide personalized service recommendations and data mining. This article firstly introduces the cross-social network user identification technology that can identify accounts belonging to the same user on different networks through user attributes, user posted content, user behavior, and network topology relationship models. Secondly, it introduces similarity calculation method of user identification technology, various algorithm performance indicators, and some recent datasets. Finally, the article points out the future research directions of cross-social network user identification technology, which should focus on the weight distribution of user attribute information, multi-dimensional data identification, and large-scale user identification.