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Can social bookmarking improve web search?
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Web Search and Web Data Mining archive
Proceedings of the international conference on Web search and web data mining table of contents
Palo Alto, California, USA
SESSION: Social search table of contents
Pages 195-206  
Year of Publication: 2008
ISBN:978-1-59593-927-9
Authors
Paul Heymann  Stanford University, Stanford, CA
Georgia Koutrika  Stanford University, Stanford, CA
Hector Garcia-Molina  Stanford University, Stanford, CA
Sponsors
ACM: Association for Computing Machinery
SIGKDD: ACM Special Interest Group on Knowledge Discovery in Data
SIGMOD: ACM Special Interest Group on Management of Data
SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web
SIGIR: ACM Special Interest Group on Information Retrieval
Publisher
ACM  New York, NY, USA
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ABSTRACT

Social bookmarking is a recent phenomenon which has the potential to give us a great deal of data about pages on the web. One major question is whether that data can be used to augment systems like web search. To answer this question, over the past year we have gathered what we believe to be the largest dataset from a social bookmarking site yet analyzed by academic researchers. Our dataset represents about forty million bookmarks from the social bookmarking site del.icio.us. We contribute a characterization of posts to del.icio. us: how many bookmarks exist (about 115 million), how fast is it growing, and how active are the URLs being posted about (quite active). We also contribute a characterization of tags used by bookmarkers. We found that certain tags tend to gravitate towards certain domains, and vice versa. We also found that tags occur in over 50 percent of the pages that they annotate, and in only 20 percent of cases do they not occur in the page text, backlink page text, or forward link page text of the pages they annotate. We conclude that social bookmarking can provide search data not currently provided by other sources, though it may currently lack the size and distribution of tags necessary to make a significant impact


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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CITED BY  21

Collaborative Colleagues:
Paul Heymann: colleagues
Georgia Koutrika: colleagues
Hector Garcia-Molina: colleagues