A Fusion Model of Multi-data Sources for User Profiling in Social Media

Abstract

User profiling in social media plays an important role in different applications. Most of the existing approaches for user profiling are based on user-generated messages, which is not sufficient for inferring user attributes. With the continuous accumulation of data in social media, integrating multi-data sources has become the inexorable trend for precise user profiling. In this paper, we take advantage of text messages, user metadata, followee information and network representations. In order to integrate seamlessly multi-data sources, we propose a novel fusion model that effectively captures the complementarity and diversity of different sources. In addition, we address the problem of friendship-based network from previous studies and introduce celebrity ties which enrich the social network and boost the connectivity of different users. Experimental results show that our method outperforms several state-of-the-art methods on a real-world dataset.