Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/18034
Title: A constrained multi-view clustering approach to influence role detection
Authors: Chen, C
Gao, D
Li, W 
Hou, Y
Issue Date: 2015
Publisher: CEUR-WS
Source: CEUR workshop proceedings, 2015, v. 1398, p. 29-34 How to cite?
Journal: CEUR Workshop Proceedings 
Abstract: Twitter has provided people with an effective way to communicate and interact with each other. It is an undisputable fact that people's influence plays an important role in disseminating information over the Twitter social network. Although a number of research work on finding influential users have been reported in the literature, they never really seek to distinguish and analyze different influence roles, which are of great value for various marketing purposes. In this paper, we move a step forward to further detect five recognized influence roles of Twitter users with regard to a particular topic. By exploring three views of features related to topic, sentiment and popularity respectively, we propose a novel constrained multi-view influence role clustering approach to group potential influential Twitter users into five categories. Experimental results demonstrate the effectiveness of the proposed approach.
Description: 1st International Workshop on Social Influence Analysis, SocInf 2015 - In conjunction with the 24th International Joint Conferences on Artificial Intelligence, IJCAI 2015, 27 July 2015
URI: http://hdl.handle.net/10397/18034
ISSN: 1613-0073
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