USER PROFILING AND PRIVACY PRESERVING FROM MULTIPLE SOCIAL NETWORKS

SONG XUEMENG

Publication

USER PROFILING AND PRIVACY PRESERVING FROM MULTIPLE SOCIAL NETWORKS

SONG XUEMENG

Abstract

User profiling, which aims to infer users' unobservable information based on observable information such as individual's behavior or utterances, is the basis for many applications. In recent years, the proliferation of social media has opened new opportunities for user profiling. Moreover, as different social networks provide different services, an increasing number of people are involved in multiple social networks, in which different aspects of users can be revealed by different social networks. Therefore, to comprehensively learn users' profiles, it is time to shift from a single social network to multiple social networks. Therefore, this thesis aims to investigate user profiling across multiple social networks. In particular, it covers studies in general scenarios of user profiling, in which a single task and multiple tasks are involved, respectively. Meanwhile, as user profiling would potentially put users at high privacy risks, this thesis also proposes a framework for privacy preservation.