Title: Social trend tracking by time series based social tagging clustering
Authors: Chen, Shihn-Yuarn
Tseng, Tzu-Ting
Ke, Hao-Ren
Sun, Chuen-Tsai
資訊工程學系
Department of Computer Science
Keywords: Web 2.0;Social tagging;Time series clustering;Event tracking
Issue Date: 15-Sep-2011
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. (C) 2011 Elsevier Ltd. All rights reserved.
URI: http://dx.doi.org/10.1016/j.eswa.2011.04.073
http://hdl.handle.net/11536/19215
ISSN: 0957-4174
DOI: 10.1016/j.eswa.2011.04.073
Journal: EXPERT SYSTEMS WITH APPLICATIONS
Volume: 38
Issue: 10
Begin Page: 12807
End Page: 12817
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