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
Link To Document :
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