User Profiling via Affinity-aware Friendship

• Main Authors: Chen, Zhuohua, ZHU, Feida, Guo, Guangming, Liu, Hongyan
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2014
• Subjects: Databases and Information Systems; Social Media
• Online Access: https://ink.library.smu.edu.sg/sis_research/2663; http://dx.doi.org/10.1007/978-3-319-13734-6_11
• Institution: Singapore Management University

The boom of online social platforms of all kinds has triggered tremendous research interest in using social network data for user profiling, which refers to deriving labels for users that characterize their various aspects. Among different kinds of user profiling approaches, one line of work has taken advantage of the high level of label similarity that is often observed among users in one’s friendship network. In this work, we identify one critical point that has been so far neglected — different users in one’s friendship network play different roles in user profiling. In particular, we categorize all users in one’s friendship network into (I) close friends whom the user knows in real life and (II) online friends with whom the user forms connection through online interaction. We propose an algorithm that is affinity-aware in inferring users’ labels through network propagation. Our divide-and-conquer framework makes the proposed method scalable to large social network data. The experiment results in three real-world datasets demonstrate the superiority of our algorithm over baselines and support our argument for affinity-awareness in label profiling.