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On Measuring Expertise in Collaborative Tagging Systems

On Measuring Expertise in Collaborative Tagging Systems
On Measuring Expertise in Collaborative Tagging Systems
Collaborative tagging systems such as Delicious.com 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 spamming than other methods such as the HITS algorithm and simple statistical measures.
collaborative tagging, expertise, folksonomy, spam, ranking
Au Yeung, Ching Man
c83390b1-d3a1-459e-8f09-01c81576e066
Noll, Michael
7e3a4879-a7b4-4a87-8190-87f46fb67904
Gibbins, Nicholas
98efd447-4aa7-411c-86d1-955a612eceac
Meinel, Christoph
fa44fc52-2724-4f9a-a391-b726e55fb3ac
Shadbolt, Nigel
5c5acdf4-ad42-49b6-81fe-e9db58c2caf7
Au Yeung, Ching Man
c83390b1-d3a1-459e-8f09-01c81576e066
Noll, Michael
7e3a4879-a7b4-4a87-8190-87f46fb67904
Gibbins, Nicholas
98efd447-4aa7-411c-86d1-955a612eceac
Meinel, Christoph
fa44fc52-2724-4f9a-a391-b726e55fb3ac
Shadbolt, Nigel
5c5acdf4-ad42-49b6-81fe-e9db58c2caf7

Au Yeung, Ching Man, Noll, Michael, Gibbins, Nicholas, Meinel, Christoph and Shadbolt, Nigel (2009) On Measuring Expertise in Collaborative Tagging Systems. Web Science Conference: Society On-Line, Greece. 18 - 20 Mar 2009. (Submitted)

Record type: Conference or Workshop Item (Paper)

Abstract

Collaborative tagging systems such as Delicious.com 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 spamming than other methods such as the HITS algorithm and simple statistical measures.

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More information

Submitted date: 18 March 2009
Additional Information: Event Dates: 18th-20th March 2009
Venue - Dates: Web Science Conference: Society On-Line, Greece, 2009-03-18 - 2009-03-20
Keywords: collaborative tagging, expertise, folksonomy, spam, ranking
Organisations: Web & Internet Science

Identifiers

Local EPrints ID: 267176
URI: https://eprints.soton.ac.uk/id/eprint/267176
PURE UUID: f702df28-5681-44c1-a5a3-196b1c48cdfe
ORCID for Nicholas Gibbins: ORCID iD orcid.org/0000-0002-6140-9956

Catalogue record

Date deposited: 09 Mar 2009 11:04
Last modified: 06 Jun 2018 12:55

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