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.
Original language | English |
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Title of host publication | SIGIR 2014 - Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval |
Publisher | Association for Computing Machinery |
Pages | 1203-1206 |
Number of pages | 4 |
ISBN (Print) | 9781450322591 |
DOIs | |
Publication status | Published - 1 Jan 2014 |
Event | 37th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2014 - Gold Coast, QLD, Australia Duration: 6 Jul 2014 → 11 Jul 2014 |
Conference
Conference | 37th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2014 |
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Country/Territory | Australia |
City | Gold Coast, QLD |
Period | 6/07/14 → 11/07/14 |
Keywords
- Influential users
- Multi-view
ASJC Scopus subject areas
- Computer Graphics and Computer-Aided Design
- Information Systems