User identification on social networks through text mining techniques: A systematic literature review

Abstract

Social connection between the set of people is known as social network analysis. People keep numerous identities on various online social sites. User-related network data has distinctive information which shows user interests, behavioral patterns, and political views. By using these behaviors individually and collectively are of great help to recognize users across social networks. SLR (Systematic Literature Review) has been performed to distinguish 31 papers published during 2010–2018. The idea is to determine user identification categories that are used to classify users. Furthermore, to identify algorithms, models, methods, and tools that has been suggested since 2010 for user characterization. We have identified 10 algorithms, 19 models, 5 methods and 8 tools that have proposed for 5 user identification categories. Finally, we empirically evaluated that text mining techniques are promising approaches for the identification of users on online social networks.