• DocumentCode
    480683
  • Title

    Link Mining for a Social Bookmarking Web Site

  • Author

    Chen, Feilong ; Scripps, Jerry ; Tan, Pang-Ning

  • Author_Institution
    Comput. Sci. & Eng., Michigan State Univ. East Lansing, East Lansing, MI
  • Volume
    1
  • fYear
    2008
  • fDate
    9-12 Dec. 2008
  • Firstpage
    169
  • Lastpage
    175
  • Abstract
    Social bookmarking tools enable users to save URLs for future reference, to create tags for annotating Web pages, and to share Web pages they found interesting with others. This paper presents a case study on the application of link mining to a social bookmarking Web site called del.icio.us. We investigated the user bookmarking and tagging behaviors and described several approaches to find surprising patterns in the data. We also identified the characteristics that made certain users more popular than others. Finally, we demonstrated the effectiveness of using social bookmarks and tags for predicting mutual ties between users.
  • Keywords
    data mining; information analysis; social networking (online); Web page annotation; delicious social bookmarking Web site; link mining; social tagging; Computer science; Fans; History; Intelligent agent; Internet; Tagging; Terminology; Uniform resource locators; Web pages; Web search; link mining; social network;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Web Intelligence and Intelligent Agent Technology, 2008. WI-IAT '08. IEEE/WIC/ACM International Conference on
  • Conference_Location
    Sydney, NSW
  • Print_ISBN
    978-0-7695-3496-1
  • Type

    conf

  • DOI
    10.1109/WIIAT.2008.369
  • Filename
    4740442