Social trend tracking by time series based social tagging clustering

Authors:
Shihn-Yuarn Chen;Tzu-Ting Tseng;Hao-Ren Ke;Chuen-Tsai Sun
Affiliations:
Department of Computer Science, National Chiao Tung University, No. 1001 Ta Hsueh Road., Hsinchu 300, Taiwan;Institute of Information Management, National Chiao Tung University, No. 1001 Ta Hsueh Road., Hsinchu 300, Taiwan;Graduate Institute of Library & Information Studies, National Taiwan Normal University, No. 162, He-ping East Road, Section 1, Taipei 10610, Taiwan;Department of Computer Science, National Chiao Tung University, No. 1001 Ta Hsueh Road., Hsinchu 300, Taiwan
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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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.