Online social platforms (OSPs), such as Facebook, Twitter, and Instagram, have grown monumentally over recent years. It was reported that as of August 2017, Facebook has over 2 billion monthly active users, while Instagram and Twitter have over 700 million and 300 million monthly active user accounts respectively. The vast amount of user-generated content and social data gathered in these behemoth platforms have made them rich data sources for academic and industrial research. However many of the existing research work has focused on analyzing and modeling users behaviors in a single platform setting, neglecting the inter-dependencies of user behaviors across multiple OSPs. In this sharing, I will present our recent work on analyzing and modeling user behaviors in multiple OSPs. In particular, I will focus on (i) analyzing users’ topical interests and platform preferences across multiple OSPs, and (ii) modeling influential users in multiple OSPs
About the Speaker
Roy Ka-Wei Lee is a Research Scientist at Living Analytics Research Centre (LARC). He obtained his Ph.D. Degree in Information Systems from the Singapore Management University in 2018 on a fully funded scholarship from the university. His research lies in the intersection of data mining, machine learning, and social computing. In particular, he is interested in studying user behaviors and information diffusion across multiple social networks. Roy will also begin his faculty journey as an Assistant Professor of Computer Science at the University of Saskatchewan from Aug 2019.