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Learning to Find Topic Experts in Twitter via Different Relations


Authors: W. Wei, G. Cong, C. Miao, F. Zhu, and G. Li
Title: Learning to Find Topic Experts in Twitter via Different Relations
Abstract: Expert finding has become a hot topic along with the flourishing of social networks, such as micro-blogging services like Twitter. Finding experts in Twitter is an important problem because tweets from experts are valuable sources that carry rich information (e.g., trends) in various domains. However, previous methods cannot be directly applied to Twitter expert finding problem. Recently, several attempts use the relations among users and Twitter Lists for expert finding. Nevertheless, these approaches only partially utilize such relations. To this end, we develop a probabilistic method to jointly exploit three types of relations (i.e., follower relation, user-list relation and list-list relation)for finding experts. Specifically, we propose a Semi-Supervised Graph-based Ranking approach (SSGR) to offline calculate the global authority of users. In SSGR, we employ a normalized Laplacian regularization term to jointly explore the three relations, which is subject to the supervised information derived from Twitter crowds. We then online compute the local relevance between users and the given query. By leveraging the global authority and local relevance of users, we rank all of users and find top-N users with highest ranking scores. Experiments on real-world data demonstrate the effectiveness of our proposed approach for topic-specific expert finding in Twitter.
Keywords: Expert search; Micro-blogging; Twitter; List; Graph-based ranking
Journal Name: IEEE Transactions on Knowledge and Data Engineering, vol. 28, no. 7
Publisher: IEEE
Year: 2016
Accepted PDF File: Learning_to_Find_Experts_in_Twitter_via_Different_Relations_accepted.pdf
Permanent Link: http://dx.doi.org/10.1109/TKDE.2016.2539166
Reference: W. Wei, G. Cong, C. Miao, F. Zhu, and G. Li, “Learning to find topic experts in twitter via different relations,” IEEE Transactions on Knowledge and Data Engineering, vol. 28, no. 7, pp. 1764–1778, July 2016.
bibtex: 
@article {LILY-j27,
   author 	= {Wei, Wei and Cong, Gao and Miao, Chunyan and Zhu, Feida and Li, Guohui},
   title 	= {Learning to Find Topic Experts in Twitter via Different Relations},
   journal 	= {IEEE Transactions on Knowledge and Data Engineering},
   year 	= {2016},
   month 	= {July},
   volume 	= {28},
   number 	= {7},
   pages 	= {1764-1778},
   publisher 	= {IEEE},
}