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Identifying experts and authoritative documents in social bookmarking systems

Grady, Jonathan P (2013) Identifying experts and authoritative documents in social bookmarking systems. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Social bookmarking systems allow people to create pointers to Web resources in a shared, Web-based environment. These services allow users to add free-text labels, or “tags”, to their bookmarks as a way to organize resources for later recall. Ease-of-use, low cognitive barriers, and a lack of controlled vocabulary have allowed social bookmaking systems to grow exponentially over time. However, these same characteristics also raise concerns. Tags lack the formality of traditional classificatory metadata and suffer from the same vocabulary problems as full-text search engines. It is unclear how many valuable resources are untagged or tagged with noisy, irrelevant tags. With few restrictions to entry, annotation spamming adds noise to public social bookmarking systems. Furthermore, many algorithms for discovering semantic relations among tags do not scale to the Web.

Recognizing these problems, we develop a novel graph-based Expert and Authoritative Resource Location (EARL) algorithm to find the most authoritative documents and expert users on a given topic in a social bookmarking system. In EARL’s first phase, we reduce noise in a Delicious dataset by isolating a smaller sub-network of “candidate experts”, users whose tagging behavior shows potential domain and classification expertise. In the second phase, a HITS-based graph analysis is performed on the candidate experts’ data to rank the top experts and authoritative documents by topic. To identify topics of interest in Delicious, we develop a distributed method to find subsets of frequently co-occurring tags shared by many candidate experts.

We evaluated EARL’s ability to locate authoritative resources and domain experts in Delicious by conducting two independent experiments. The first experiment relies on human judges’ n-point scale ratings of resources suggested by three graph-based algorithms and Google. The second experiment evaluated the proposed approach’s ability to identify classification expertise through human judges’ n-point scale ratings of classification terms versus expert-generated data.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Grady, Jonathan Pjpg14@pitt.eduJPG14
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairSpring, Michael Bspring@pitt.eduSPRING
Committee MemberBrusilovsky, Peterpeterb@pitt.eduPETERB
Committee MemberButler, Brian
Committee MemberHe, Daqingdah44@pitt.eduDAH44
Committee MemberHirtle, Stephen C.hirtle@pitt.eduHIRTLE
Date: 2 May 2013
Date Type: Publication
Defense Date: 27 March 2013
Approval Date: 2 May 2013
Submission Date: 26 April 2013
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 183
Institution: University of Pittsburgh
Schools and Programs: School of Information Sciences > Information Science
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: social networks, social bookmarks, social tags, web science, web classification, expertise
Date Deposited: 02 May 2013 15:43
Last Modified: 15 Nov 2016 14:12


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