Inferring topic-dependent influence roles of Twitter users

Chengyao Chen, Dehong Gao, Wenjie Li, Yuexian Hou

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

22 Citations (Scopus)

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 languageEnglish
Title of host publicationSIGIR 2014 - Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval
PublisherAssociation for Computing Machinery
Pages1203-1206
Number of pages4
ISBN (Print)9781450322591
DOIs
Publication statusPublished - 1 Jan 2014
Event37th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2014 - Gold Coast, QLD, Australia
Duration: 6 Jul 201411 Jul 2014

Conference

Conference37th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2014
CountryAustralia
CityGold Coast, QLD
Period6/07/1411/07/14

Keywords

  • Influential users
  • Multi-view
  • Twitter

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Information Systems

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