The Web Science Trust

On Measuring Expertise in Collaborative Tagging Systems

Man Au Yeung, Ching and Noll, Michael and Gibbins, Nicholas and Meinel, Christoph and Shadbolt, Nigel (2009) On Measuring Expertise in Collaborative Tagging Systems. In: Proceedings of the WebSci'09: Society On-Line, 18-20 March 2009, Athens, Greece.

[img] PDF (preprint) - Repository staff only - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
19Kb
[img]
Preview
PDF (Final Version) - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
257Kb
[img]
Preview
PDF (Presentation) - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
2695Kb

Abstract

Collaborative tagging systems such as Delicious provide a new means of organizing and sharing resources. They also allow users to search for documents relevant to a particular topic or for other users who are experts in a particular domain. Nevertheless, identifying relevant documents and knowledgeable users is not a trivial task, especially when the volume of documents is huge and there exist spamming activities. In this paper, we discuss the notions of experts and expertise in the context of collaborative tagging systems. We propose that the level of expertise of a user in a particular topic is mainly determined by two factors: (1) there should be a relationship of mutual reinforcement between the expertise of a user and the quality of a document; and (2) an expert should be one who tends to identify useful documents before other users discover them. We propose a graph-based algorithm, SPEAR (SPamming-resistant Expertise Analysis and Ranking), which implements the above ideas for ranking users in a collaborative tagging system. We carry out experiments on both simulated data sets and real-world data sets obtained from Delicious, and show that SPEAR is more resistant to spammers than other methods such as the HITS algorithm and simple statistical measures.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:collaborative tagging, folksonomies, expertise, ranking algorithm
Subjects:Web Science Events > Web Science 2009
ID Code:109
Deposited By: W S T Administrator
Deposited On:24 Jan 2009 08:45
Last Modified:25 Oct 2011 16:51

Repository Staff Only: item control page

EPrints Logo
Web Science Repository is powered by EPrints 3 which is developed by the School of Electronics and Computer Science at the University of Southampton. More information and software credits.