Telling experts from spammers: expertise ranking in folksonomies
Telling experts from spammers: expertise ranking in folksonomies
With a suitable algorithm for ranking the expertise of a user in a collaborative tagging system, we will be able to identify experts and discover useful and relevant resources through them. We propose that the level of expertise of a user with respect to a particular topic is mainly determined by two factors. Firstly, an expert should possess a high quality collection of resources, while the quality of a Web resource depends on the expertise of the users who have assigned tags to it. Secondly, an expert should be one who tends to identify interesting or useful resources before other users do. We propose a graph-based algorithm, SPEAR (SPamming-resistant Expertise Analysis and Ranking), which implements these ideas for ranking users in a folksonomy. We evaluate our method with experiments on data sets collected from Delicious.com comprising over 71,000 Web documents, 0.5 million users and 2 million shared bookmarks. We also show that the algorithm is more resistant to spammers than other methods such as the original HITS algorithm and simple statistical measures.
collaborative tagging, folksonomy, expertise, ranking, spam
978-1-60558-483-6
612-619
Noll, Michael
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Au Yeung, Ching Man
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Gibbins, Nicholas
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Meinel, Christoph
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Shadbolt, Nigel
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19 July 2009
Noll, Michael
7e3a4879-a7b4-4a87-8190-87f46fb67904
Au Yeung, Ching Man
cae7c811-0259-46b1-8400-bf8e70815a1c
Gibbins, Nicholas
98efd447-4aa7-411c-86d1-955a612eceac
Meinel, Christoph
fa44fc52-2724-4f9a-a391-b726e55fb3ac
Shadbolt, Nigel
5c5acdf4-ad42-49b6-81fe-e9db58c2caf7
Noll, Michael, Au Yeung, Ching Man, Gibbins, Nicholas, Meinel, Christoph and Shadbolt, Nigel
(2009)
Telling experts from spammers: expertise ranking in folksonomies.
Proceedings of the 32nd Annual ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'09), Boston, United States.
19 - 23 Jul 2009.
.
(doi:10.1145/1571941.1572046).
Record type:
Conference or Workshop Item
(Paper)
Abstract
With a suitable algorithm for ranking the expertise of a user in a collaborative tagging system, we will be able to identify experts and discover useful and relevant resources through them. We propose that the level of expertise of a user with respect to a particular topic is mainly determined by two factors. Firstly, an expert should possess a high quality collection of resources, while the quality of a Web resource depends on the expertise of the users who have assigned tags to it. Secondly, an expert should be one who tends to identify interesting or useful resources before other users do. We propose a graph-based algorithm, SPEAR (SPamming-resistant Expertise Analysis and Ranking), which implements these ideas for ranking users in a folksonomy. We evaluate our method with experiments on data sets collected from Delicious.com comprising over 71,000 Web documents, 0.5 million users and 2 million shared bookmarks. We also show that the algorithm is more resistant to spammers than other methods such as the original HITS algorithm and simple statistical measures.
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sigir09-tagexpert.pdf
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Published date: 19 July 2009
Venue - Dates:
Proceedings of the 32nd Annual ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'09), Boston, United States, 2009-07-19 - 2009-07-23
Keywords:
collaborative tagging, folksonomy, expertise, ranking, spam
Organisations:
Web & Internet Science
Identifiers
Local EPrints ID: 267327
URI: http://eprints.soton.ac.uk/id/eprint/267327
ISBN: 978-1-60558-483-6
PURE UUID: 8e4ad102-4aeb-43ce-92a4-184e44a37ca1
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Date deposited: 04 May 2009 18:44
Last modified: 15 Mar 2024 02:59
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Contributors
Author:
Michael Noll
Author:
Ching Man Au Yeung
Author:
Nicholas Gibbins
Author:
Christoph Meinel
Author:
Nigel Shadbolt
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