Title

Learning to Find Topic Experts in Twitter via Different Relations

Publication Type

Journal Article

Publication Date

3-2016

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, Graph-based ranking, List, Micro-blogging, Twitter

Discipline

Digital Communications and Networking

Research Areas

Data Management and Analytics

Publication

IEEE Transactions on Knowledge and Data Engineering

Volume

PP

Issue

99

ISSN

1041-4347

Identifier

10.1109/TKDE.2016.2539166

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

This document is currently not available here.

Share

COinS