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SPEAR: spamming-resistant expertise analysis and ranking in collaborative tagging systems

SPEAR: spamming-resistant expertise analysis and ranking in collaborative tagging systems
SPEAR: spamming-resistant expertise analysis and ranking in collaborative tagging systems
In this article, we discuss the notions of experts and expertise in resource discovery in the context of collaborative tagging systems. We propose that the level of expertise of a user with respect to a particular topic is mainly determined by two factors. First, an expert should possess a high-quality collection of resources, while the quality of a Web resource in turn depends on the expertise of the users who have assigned tags to it, forming a mutual reinforcement relationship. Second, an expert should be one who tends to identify interesting or useful resources before other users discover them, thus bringing these resources to the attention of the community of users. We propose a graph-based algorithm, SPEAR (spamming-resistant expertise analysis and ranking), which implements the above ideas for ranking users in a folksonomy. Our experiments show that our assumptions on expertise in resource discovery, and SPEAR as an implementation of these ideas, allow us to promote experts and demote spammers at the same time, with performance significantly better than the original hypertext-induced topic search algorithm and simple statistical measures currently used in most collaborative tagging systems.
collaborative, tagging, expertise, folksonomy, hits, rankin, spamming
458-488
Au Yeung, Ching-man
4154359c-c629-46bd-9144-49826e7251c2
Noll, Michael G.
83557e76-db0a-4fa1-af1d-744b59c4cb3e
Gibbins, Nicholas
98efd447-4aa7-411c-86d1-955a612eceac
Meine, Christoph
b2bc9633-43d7-422a-8d04-be16a2e74ce3
Shadbolt, Nigel
5c5acdf4-ad42-49b6-81fe-e9db58c2caf7
Au Yeung, Ching-man
4154359c-c629-46bd-9144-49826e7251c2
Noll, Michael G.
83557e76-db0a-4fa1-af1d-744b59c4cb3e
Gibbins, Nicholas
98efd447-4aa7-411c-86d1-955a612eceac
Meine, Christoph
b2bc9633-43d7-422a-8d04-be16a2e74ce3
Shadbolt, Nigel
5c5acdf4-ad42-49b6-81fe-e9db58c2caf7

Au Yeung, Ching-man, Noll, Michael G., Gibbins, Nicholas, Meine, Christoph and Shadbolt, Nigel (2011) SPEAR: spamming-resistant expertise analysis and ranking in collaborative tagging systems. Computational Intelligence, 27 (3), 458-488. (doi:10.1111/j.1467-8640.2011.00384.x).

Record type: Article

Abstract

In this article, we discuss the notions of experts and expertise in resource discovery in the context of collaborative tagging systems. We propose that the level of expertise of a user with respect to a particular topic is mainly determined by two factors. First, an expert should possess a high-quality collection of resources, while the quality of a Web resource in turn depends on the expertise of the users who have assigned tags to it, forming a mutual reinforcement relationship. Second, an expert should be one who tends to identify interesting or useful resources before other users discover them, thus bringing these resources to the attention of the community of users. We propose a graph-based algorithm, SPEAR (spamming-resistant expertise analysis and ranking), which implements the above ideas for ranking users in a folksonomy. Our experiments show that our assumptions on expertise in resource discovery, and SPEAR as an implementation of these ideas, allow us to promote experts and demote spammers at the same time, with performance significantly better than the original hypertext-induced topic search algorithm and simple statistical measures currently used in most collaborative tagging systems.

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More information

Published date: August 2011
Keywords: collaborative, tagging, expertise, folksonomy, hits, rankin, spamming
Organisations: Web & Internet Science

Identifiers

Local EPrints ID: 273196
URI: https://eprints.soton.ac.uk/id/eprint/273196
PURE UUID: a9101aa9-bf86-48d3-870f-bd690e0e15bd
ORCID for Nicholas Gibbins: ORCID iD orcid.org/0000-0002-6140-9956

Catalogue record

Date deposited: 06 Feb 2012 16:28
Last modified: 20 Jul 2019 01:11

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Contributors

Author: Ching-man Au Yeung
Author: Michael G. Noll
Author: Nicholas Gibbins ORCID iD
Author: Christoph Meine
Author: Nigel Shadbolt

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