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http://dx.doi.org/10.3837/tiis.2016.06.010

Identifying Topic-Specific Experts on Microblog  

Yu, Yan (Computer Science Department, Southeast University Chengxian College)
Mo, Lingfei (School of Instrument Science and Engineering, Southeast University)
Wang, Jian (Department of Radiology, Changhai Hospital)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.10, no.6, 2016 , pp. 2627-2647 More about this Journal
Abstract
With the rapid growth of microblog, expert identification on microblog has been playing a crucial role in many applications. While most previous expert identification studies only assess global authoritativeness of a user, there is no way to differentiate the authoritativeness in a particular aspect of topics. In this paper, we propose a novel model, which jointly models text and following relationship in the same generative process. Furthermore, we integrate a similarity-based weight scheme into the model to address the popular bias problem, and use followee topic distribution as prior information to make user's topic distribution more precisely. Our empirical study on two large real-world datasets shows that our proposed model produces significantly higher quality results than the prior arts.
Keywords
Microblog; expert identification; topic-specific expert; LDA; similarity;
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