22 - Social Web Social Search Social search Communities of...

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Unformatted text preview: Social Web Social Search Social search Communities of users actively participating in the search process Goes beyond classical search tasks Key differences Users interact with the system Users interact with other users either implicitly or explicitly Web 2.0, 3.0, ... Social search includes, but is not limited to,the so called social media sites Collectively referred to as Web 2.0 as opposed to the classical notion of the Web (Web 1.0) Social media sites User generated content Users can tag their own and others content Users can share favorites, tags, etc., with others Examples: Digg, Twitter, Flickr, YouTube, Del.icio.us, CiteULike, MySpace, Facebook, and LinkedIn User Tags and Manual Indexing Then: Library card catalogs Indexing terms chosen with search in mind Experts generate indexing terms Terms are very high quality Terms chosen from controlled vocabulary Now: Social media tagging Tags not always chosen with search in mind Users generate tags Tags can be noisy or even incorrect Tags chosen from folksonomies Can Social Bookmarking Improve Web Search? Paul Heymann, Georgia Koutrika, and Hector Garcia-Molina Department of Computer Science Stanford University February 12th, 2008 Outline Introduction Problem Statement Data Gathering Methodology Analysis Conclusions YouTube Amazon del.icio.us Outline Introduction Problem Statement Data Gathering Methodology Analysis Conclusions Problem Statement Can social bookmarking improve web search? Subproblems Subproblems Are there enough URLs? Are there enough tags? Are the URLs valuable ? Are the tags redundant ? Tags versus Other Content Outline Introduction Problem Statement Data Gathering Methodology Analysis Conclusions del.icio.us posts Bookmarks/Posts Triples paul : news , uk bbc.co.uk ( paul , news , bbc.co.uk ) 08:33:25 ( paul , uk , bbc.co.uk ) mary : recipes , food food.com ( mary , recipes , food.com ) 08:33:23 ( mary , food , food.com ) dave : tv , cnn , news cnn.com ( dave , tv , cnn.com ) 08:33:21 ( dave , cnn , cnn.com ) ( dave , news , cnn.com ) Realtime Web Crawling Outline Introduction Problem Statement Data Gathering Methodology Analysis Conclusions Size and Growth 120 thousand ( 10 5 ) posts/day (versus 10 6 blog posts/day) 60150 million posts 1275 million ( 10 7 10 8 ) unique URLs (versus 10 9 10 11 total URLs) Date Estimated Number of Posts August 1, 2005 December 9, 2005 August 16, 2006 30000 60000 90000 120000 Date Estimated Number of Posts November 11, 2006 July 5, 2007 30000 60000 90000 120000 150000 URL Indexing and Age Found Initially 57.5% Indexed Within 4 Weeks 12.75% Indexed Within 6 Months 12.75% Never Indexed 17% Of the 57.5% found initially, modification time at time of post: URL Indexing and Age Found Initially 57.5% Indexed Within 4 Weeks 12.75% Indexed Within 6 Months 12.75% Never Indexed 17% Of the 57.5% found initially, modification time at time of post: Tagging Caveats (The Tagging 6)...
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This note was uploaded on 01/21/2011 for the course CSCP 689 taught by Professor James during the Spring '10 term at Texas A&M.

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22 - Social Web Social Search Social search Communities of...

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