Publication Type

PhD Dissertation

Version

publishedVersion

Publication Date

11-2018

Abstract

This dissertation addresses the empirical analysis on user-generated data from multiple online social platforms (OSPs) and modeling of latent user factors in multiple OSPs setting.

In the first part of this dissertation, we conducted cross-platform empirical studies to better understand user's social and work activities in multiple OSPs. In particular, we proposed new methodologies to analyze users' friendship maintenance and collaborative activities in multiple OSPs. We also apply the proposed methodologies on real-world OSP datasets, and the findings from our empirical studies have provided us with a better understanding on users' social and work activities which are previously not uncovered in single OSP studies.

In the second part of this dissertation, we developed user modeling techniques to learn latent user factors in multiple OSPs setting. In particular, we proposed generative models to learn the user topical interests, topic-specific platform preferences and influences in multiple OSPs setting. The proposed models are also applied to real-world OSPs datasets to profile user topical interests and identify influential users in multiple OSPs. The designed generative models are also generalizable and can be applied to different cross-OSP datasets.

Keywords

online social network, social network analysis, data mining, user modeling, social computing

Degree Awarded

PhD in Information Systems

Discipline

Computer and Systems Architecture | Digital Communications and Networking | Numerical Analysis and Scientific Computing

Supervisor(s)

LIM, Ee Peng

Publisher

Singapore Management University

City or Country

Singapore

Copyright Owner and License

Author

Download

Share

COinS