Social User Profiling: A Social-Aware Topic Modeling Perspective


Social user profiling is an analytical process that delivers an in-depth blueprint of users' personal characteristics in social networks, which can enable a wide range of applications, such as personalized recommendation and targeted marketing. While social user profiling has attracted a lot of attention in the past few years, it is still very challenging to collaboratively model both user-centric information and social network structure. To this end, in this paper we develop an analytic framework for solving the social user profiling problem. Specifically, we first propose a novel social-aware semi-supervised topic model, i.e., User Profiling based Topic Model (UPTM), which can reconcile the observed user characteristics and social network structure for discovering the latent reasons behind social connections and further extracting users' potential profiles. In addition, to improve the profiling performance, we further develop a label propagation strategy for refining the profiling results of UPTM. Finally, we conduct extensive evaluations with a variety of real-world data, where experimental results demonstrate the effectiveness of our proposed modeling method.

Meeting Name

22nd International Conference on Database Systems for Advanced Applications, DASFAA 2017 (2017: Mar. 27-30, Suzhou, China)


Computer Science


This research was partially supported by grants from the National Natural Science Foundation of China (NSFC, Grant No. U1605251), the National Science Foundation for Distinguished Young Scholars of China (Grant No. 61325010), and the NSFC Major research program (Grant No. 91546103).

Keywords and Phrases

Data mining; Database systems; Social networking (online); Analytical process; Personal characteristics; Personalized recommendation; Social network structures; Targeted marketing; Topic Modeling; User characteristics; User profiling; Depth profiling; Social network; Topic model

International Standard Book Number (ISBN)


International Standard Serial Number (ISSN)

0302-9743; 1611-3349

Document Type

Article - Conference proceedings

Document Version


File Type





© 2017 Springer Verlag, All rights reserved.

Publication Date

01 Mar 2017