Identifying multiple social network accounts belonging to the same users - Mungen, Ahmet Anil; Gundogan, Esra; Kaya, Mehmet (Springer Wien, 2021)
Social networks used for different purposes by users contain different user data. Finding same users' accounts in different social networks and compiling the data found in a single repository will be a very important factor that will both improve the recommended systems and increase the user experience. The aim of this study is to collect the data of thousands of users in nine different social networks and to find the same users in these networks. For this purpose, the novel node alignment and node similarity methods were proposed in the study. While using the anchor method for topological-based node proposition, density relationships between connections are also taken into account. In the node similarity method, the number of successful node matching was increased with attribute selection criteria and initial state selection method we proposed. However, in this study, alignment and similarity were determined both according to users' profile characteristics and their relationship with other users. Nine different methods have been proposed for finding the same accounts on different social networks. The proposed methods were tested on the data of two to six social networks, and users' match success rates were measured. The results showed success rates of up to 95%. This enabled the creation of a wide user profile covering multiple social networks for users whose different attributes are gathered on the same graph in multiple social networks.
Keywords: Multiple social networks; Identifying the same users; Node alignment; Vector-based similarity algorithm