Discovering Trends in Collaborative Tagging Systems

Authors:
Aaron Sun;Daniel Zeng;Huiqian Li;Xiaolong Zheng
Affiliations:
Department of Management Information Systems, University of Arizona, Tucson,;Department of Management Information Systems, University of Arizona, Tucson,;The Key Lab of Complex Systems and Intelligence Science, Institute of Automation, Chinese Academy of Sciences, China;The Key Lab of Complex Systems and Intelligence Science, Institute of Automation, Chinese Academy of Sciences, China
Venue:
PAISI, PACCF and SOCO '08 Proceedings of the IEEE ISI 2008 PAISI, PACCF, and SOCO international workshops on Intelligence and Security Informatics
Year:
2008

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Abstract

Collaborative tagging systems (CTS) offer an interesting social computing application context for topic detection and tracking research. In this paper, we apply a statistical approach for discovering topic-specific bursts from a popular CTS - del.icio.us. This approach allows trend discovery from different components of the system such as users, tags, and resources. Based on the detected topic bursts, we perform a preliminary analysis of related burst formation patterns. Our findings indicate that users and resources contributing to the bursts can be classified into two categories: old and new, based on their past usage histories. This classification scheme leads to interesting empirical findings.