Discovering trends in collaborative tagging systems

Aaron Sun, Daniel Zeng, Huiqian Li, Xiaolong Zheng

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationIntelligence and Security Informatics - IEEE ISI 2008 International Workshops
Subtitle of host publicationPAISI, PACCF, and SOCO 2008, Proceedings
Pages377-383
Number of pages7
DOIs
StatePublished - Jul 1 2008
EventIEEE International Conference on Intelligence and Security Informatics, ISI 2008 Workshops: PAISI, PACCF, and SOCO 2008 - Taipei, Taiwan, Province of China
Duration: Jun 17 2008Jun 17 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5075 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

OtherIEEE International Conference on Intelligence and Security Informatics, ISI 2008 Workshops: PAISI, PACCF, and SOCO 2008
CountryTaiwan, Province of China
CityTaipei
Period6/17/086/17/08

Keywords

  • Burst
  • Collaborative tagging
  • Trend discovery

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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