MLLDA: multi-level LDA for modelling users on content curation social networks

User analysis is an important part of social network analysis. Most existing studies model users separately using either user-generated contents or social links among users. In this paper we propose to model users on the Content Curation Social Network (CCSN) in a unified framework by mining user-generated contents as well as social links. We propose a latent Bayesian model Multi-level LDA (MLLDA) that represents users with latent user interests discovered from user-contributed textual description and social links formed by information sharing. We demonstrate that MLLDA can produce accurate user models for community discovery and recommendation on the CCSN.