A Method to Classify Users on Social Networks Based on Similarity Mesuring

Abstract

With the express growth of social networks, users have joined more and more of these networks and live their lives virtually. Consequently, they create huge amounts of data on these social networks: their profile, interests, and behaviors such as posting, commenting, liking, joining groups or communities, etc. One of the basic issues in these challenges is the problem of estimating the similarity among users on these social networks based on their profile, interests, and behavior. This paper presents a model for estimating the similarity between users based ontheir behavior on social networks. The considered behaviors are activities including posting or sharing entries, liking these entries, commenting and liking the comments in these entries, and joining a group in the social networks. The model is then evaluated with a dataset collected from Facebook users. The results show that the model correctly estimates the similarity among users in the majority of cases.