Learning to Find Topic Experts in Twitter via Different Relations
Wei Wei, Gao Cong, Chunyan Miao, Feida Zhu, Guohui Li · IEEE Transactions on Knowledge and Data Engineering · 2016
Expert finding has become a hot topic along with the flourishing of social networks, such as micro-blogging services like Twitter. Finding experts inTwitteris 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 toTwitterexpert finding problem. Recently, several attempts use the relations among users andTwitter 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.,followerrelation,user-listrelation, andlist-listrelation) for finding experts. Specifically, we propose aSemi-SupervisedGraph-basedRanking approach ($\sf{SSGR}$) to offline calculate theglobal authorityof users. In$\sf{SSGR}$, 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 thelocal relevancebetween users and the given query. By leveraging theglobal authorityandlocal relevanceof 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 fortopic-specificexpert finding inTwitter.