Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/129139
Title: USER PROFILING AND PRIVACY PRESERVING FROM MULTIPLE SOCIAL NETWORKS
Authors: SONG XUEMENG
Keywords: multi-social networks, user profiling, privacy preserving, user interest inference, multi-task learning
Issue Date: 1-Jul-2016
Citation: SONG XUEMENG (2016-07-01). USER PROFILING AND PRIVACY PRESERVING FROM MULTIPLE SOCIAL NETWORKS. ScholarBank@NUS Repository.
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.
URI: http://scholarbank.nus.edu.sg/handle/10635/129139
Appears in Collections:Ph.D Theses (Open)

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