Identifying User Profiles Via User Footprints

Abstract

User identification has been a major field of research in privacy and security topics. Users might utilize multiple Online Social Networks (OSNs) to access a variety of text, videos, and links, and connect to their friends. Identifying user profiles corresponding to multiple virtual activities of users across social networks is significant for the development of related fields, such as network security, user behavior patterns analysis, and user recommendation systems. In addition, predicting personal attributes based on public content is a challenging topic. In this work, we perform an empirical study and proposed a scheme with considerable performance. In this work, we investigate Reddit, a famous social network for questioning and answering. By considering available personal and non-personal attributes, we discuss our main findings based on mapping the different features such as user activities to a special user profile. we collected a dataset with wide distribution consisting of 5000 samples. To map non-personal attributes to personal attributes, a classification approach based on support vector machines (SVM), Random Forests (RF), and deep belief network has been used. Experimental results demonstrate the effectiveness of the proposed methodology and achieved classification accuracy higher than 89%.