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Title: Inferring topic-dependent influence roles of Twitter users
Authors: Chen, C
Gao, D
Li, W 
Hou, Y
Keywords: Influential users
Issue Date: 2014
Publisher: Association for Computing Machinery
Source: SIGIR 2014 - Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2014, p. 1203-1206 How to cite?
Abstract: Twitter, as one of the most popular social media platforms, provides a convenient way for people to communicate and interact with each other. It has been well recognized that influence exists during users' interactions. Some pioneer studies on finding influential users have been reported in the literature, but they do not distinguish different influence roles, which are of great value for various marketing purposes. In this paper, we move a step forward trying to further distinguish influence roles of Twitter users in a certain topic. By defining three views of features relating to topic, sentiment and popularity respectively, we propose a Multi-view Influence Role Clustering (MIRC) algorithm to group Twitter users into five categories. Experimental results show the effectiveness of the proposed approach in inferring influence roles.
Description: 37th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2014, Gold Coast, QLD, 6-11 July 2014
ISBN: 9781450322591
DOI: 10.1145/2600428.2609545
Appears in Collections:Conference Paper

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