Social trend tracking by time series based social tagging clustering

Shihn Yuarn Chen, Tzu Ting Tseng, Hao Ren Ke, Chuen Tsai Sun

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

Social tagging is widely practiced in the Web 2.0 era. Users can annotate useful or interesting Web resources with keywords for future reference. Social tagging also facilitates sharing of Web resources. This study reviews the chronological variation of social tagging data and tracks social trends by clustering tag time series. The data corpus in this study is collected from Hemidemi.com. A tag is represented in a time series form according to its annotating Web pages. Then time series clustering is applied to group tag time series with similar patterns and trends in the same time period. Finally, the similarities between clusters in different time periods are calculated to determine which clusters have similar themes, and the trend variation of a specific tag in different time periods is also analyzed. The evaluation shows the recommendation accuracy of the proposed approach is about 75%. Besides, the case discussion also proves the proposed approach can track the social trends.

Original languageEnglish
Pages (from-to)12807-12817
Number of pages11
JournalExpert Systems with Applications
Volume38
Issue number10
DOIs
Publication statusPublished - 2011 Sep 15

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Keywords

  • Event tracking
  • Social tagging
  • Time series clustering
  • Web 2.0

ASJC Scopus subject areas

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

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