Resource discovery through social tagging : a classification and content analytic approach

DSpace/Manakin Repository


Search DR-NTU

Advanced Search Subject Search


My Account

Resource discovery through social tagging : a classification and content analytic approach

Show simple item record

dc.contributor.author Goh, Dion Hoe-Lian
dc.contributor.author Chua, Alton Yeow Kuan
dc.contributor.author Lee, Chei Sian
dc.contributor.author Razikin, Khasfariyati
dc.date.accessioned 2012-07-30T02:43:24Z
dc.date.available 2012-07-30T02:43:24Z
dc.date.copyright 2008
dc.date.issued 2012-07-30
dc.identifier.citation Goh, D. H. L., Chua, A. Y. K., Lee, C. S., & Razikin, K. (2009). Resource discovery through social tagging: a classification and content analytic approach. Online Information Review, 33(3), 568–583.
dc.identifier.issn 1468-4527
dc.identifier.uri http://hdl.handle.net/10220/8348
dc.description.abstract Purpose – Social tagging systems allow users to assign keywords (tags) to useful resources, facilitating their future access by the tag creator and possibly by other users. Social tagging has both proponents and critics, and this paper aims to investigate if tags are an effective means of resource discovery. Design/methodology/approach – The paper adopts techniques from text categorisation in which webpages and their associated tags from del.icio.us and trained Support Vector Machine (SVM) classifiers are downloaded to determine if the documents could be assigned to their associated tags. Two text categorisation experiments were conducted. The first used only the terms from the documents as features while the second experiment included tags in addition to terms as part of its feature set. Performance metrics used were precision, recall, accuracy and F1 score. A content analysis was also conducted to uncover characteristics of effective and ineffective tags for resource discovery. Findings – Results from the classifiers were mixed, and the inclusion of tags as part of the feature set did not result in a statistically significant improvement (or degradation) of the performance of the SVM classifiers. This suggests that not all tags can be used for resource discovery by public users, confirming earlier work that there are many dynamic reasons for tagging documents that may not be apparent to others. Originality/value – The authors extend their understanding of social classification and its utility in sharing and accessing resources. Results of this work may be used to guide development in social tagging systems as well as social tagging practices.
dc.language.iso en
dc.relation.ispartofseries Online information review
dc.rights © 2008 Emerald Group Publishing Limited. This is the author created version of a work that has been peer reviewed and accepted for publication by Online Information Review, Emerald Group Publishing Limited. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [http://dx.doi.org/10.1108/14684520910969961].
dc.subject DRNTU::Library and information science::General.
dc.title Resource discovery through social tagging : a classification and content analytic approach
dc.type Journal Article
dc.contributor.school Wee Kim Wee School of Communication and Information
dc.identifier.doi http://dx.doi.org/10.1108/14684520910969961
dc.description.version Accepted version

Files in this item

Files Size Format View
10. Resource Di ... tent Analytic Approach.pdf 274.6Kb PDF View/Open

This item appears in the following Collection(s)

Show simple item record


Total views

All Items Views
Resource discovery through social tagging : a classification and content analytic approach 443

Total downloads

All Bitstreams Views
10. Resource Discovery through Social Tagging A Classification and Content Analytic Approach.pdf 528

Top country downloads

Country Code Views
United States of America 118
Singapore 74
China 59
Thailand 35
France 24

Top city downloads

city Views
Singapore 73
Mountain View 46
Bangkok 20
Beijing 10
Chicago 8