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Detecting Trends in Social Bookmarking Systems: A del.icio.us Endeavor

Author

Listed:
  • Robert Wetzker

    (Technische Universität Berlin, Germany)

  • Carsten Zimmermann

    (University of San Diego, USA)

  • Christian Bauckhage

    (University of Bonn and Fraunhofer IAIS, Germany)

Abstract

The authors present and evaluate an approach to trend detection in social bookmarking systems using a probabilistic generative model in combination with smoothing techniques. Social bookmarking systems are gaining major interest among researchers in the areas of data mining and Web intelligence, since they provide a large amount of user-generated annotations and reflect the interest of millions of people. Based on a vast corpus of approximately 150 million bookmarks found at del. icio.us, the authors analyze bookmarking and tagging patterns and discuss evidence that social bookmarking systems are vulnerable to spamming. They present a method to limit the impact of spam on a trend detector and provide conclusions as well as directions for future research.

Suggested Citation

  • Robert Wetzker & Carsten Zimmermann & Christian Bauckhage, 2010. "Detecting Trends in Social Bookmarking Systems: A del.icio.us Endeavor," International Journal of Data Warehousing and Mining (IJDWM), IGI Global, vol. 6(1), pages 38-57, January.
    • Handle: RePEc:igg:jdwm00:v:6:y:2010:i:1:p:38-57
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    Citations

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    Cited by:

    1. Ibrahim Sorkhoh & Khaled A. Mahdi & Maytham Safar, 2013. "Estimation algorithm for counting periodic orbits in complex social networks," Information Systems Frontiers, Springer, vol. 15(2), pages 193-202, April.
    2. Christian Mühlroth & Michael Grottke, 2018. "A systematic literature review of mining weak signals and trends for corporate foresight," Journal of Business Economics, Springer, vol. 88(5), pages 643-687, July.

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