research-article

Telling experts from spammers: expertise ranking in folksonomies

Authors:

Michael G. Noll,

Ching-man Au Yeung,

Nicholas Gibbins,

Christoph Meinel,

Nigel ShadboltAuthors Info & Claims

SIGIR '09: Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval

Pages 612 - 619

https://doi.org/10.1145/1571941.1572046

Published: 19 July 2009 Publication History

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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Cited By

Martinelli FMercaldo FSantone A(2019)Social Network Polluting Contents Detection through Deep Learning Techniques2019 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN.2019.8852080(1-10)Online publication date: Jul-2019
https://doi.org/10.1109/IJCNN.2019.8852080
hamid iWu YNawaz QZhao R(2017)An improved attributed graph clustering method for discovering expert role in real-world communitiesProceedings of the 10th EAI International Conference on Mobile Multimedia Communications10.4108/eai.13-7-2017.2270341(249-255)Online publication date: 8-Dec-2017
https://dl.acm.org/doi/10.4108/eai.13-7-2017.2270341
Lim WCarman M(2017)Annotator Expertise and Information Quality in Annotation-based RetrievalProceedings of the 22nd Australasian Document Computing Symposium10.1145/3166072.3166075(1-8)Online publication date: 7-Dec-2017
https://dl.acm.org/doi/10.1145/3166072.3166075
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Index Terms

Telling experts from spammers: expertise ranking in folksonomies
1. Information systems
  1. Information retrieval
  2. Information storage systems

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The Spamming-Resistant Expertise Analysis and Ranking (Spear) algorithm is more resistant to spammers than other methods such as the HITS algorithm and other statistical measures for collaborative tagging systems.
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Collaborative tagging describes the process by which many users add metadata in the form of keywords to shared content. Recently, collaborative tagging has grown in popularity on the web, on sites that allow users to tag bookmarks, photographs and other ...
Survey on social tagging techniques

Social tagging on online portals has become a trend now. It has emerged as one of the best ways of associating metadata with web objects. With the increase in the kinds of web objects becoming available, collaborative tagging of such objects is also ...

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Published In

cover image ACM Conferences

SIGIR '09: Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval

July 2009

896 pages

ISBN:9781605584836

DOI:10.1145/1571941

General Chairs:
James Allan
University of Massachusetts Amherst, USA
,
Javed Aslam
Northeastern University, USA
,
Program Chairs:
Mark Sanderson
University of Sheffield, UK
,
ChengXiang Zhai
University of Illinois at Urbana-Champaign, USA
,
Justin Zobel
University of Melbourne, Australia

Copyright © 2009 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 19 July 2009

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SIGIR '09: The 32nd International ACM SIGIR conference on research and development in Information Retrieval

July 19 - 23, 2009

MA, Boston, USA

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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

Martinelli FMercaldo FSantone A(2019)Social Network Polluting Contents Detection through Deep Learning Techniques2019 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN.2019.8852080(1-10)Online publication date: Jul-2019
https://doi.org/10.1109/IJCNN.2019.8852080
hamid iWu YNawaz QZhao R(2017)An improved attributed graph clustering method for discovering expert role in real-world communitiesProceedings of the 10th EAI International Conference on Mobile Multimedia Communications10.4108/eai.13-7-2017.2270341(249-255)Online publication date: 8-Dec-2017
https://dl.acm.org/doi/10.4108/eai.13-7-2017.2270341
Lim WCarman M(2017)Annotator Expertise and Information Quality in Annotation-based RetrievalProceedings of the 22nd Australasian Document Computing Symposium10.1145/3166072.3166075(1-8)Online publication date: 7-Dec-2017
https://dl.acm.org/doi/10.1145/3166072.3166075
Hong MJung JCamacho D(2017)GRSATCybernetics and Systems10.1080/01969722.2016.127677048:3(140-161)Online publication date: 1-Apr-2017
https://dl.acm.org/doi/10.1080/01969722.2016.1276770
Lim WCarman MWong S(2016)Estimating Domain-Specific User Expertise for Answer Retrieval in Community Question-Answering PlatformsProceedings of the 21st Australasian Document Computing Symposium10.1145/3015022.3015032(33-40)Online publication date: 5-Dec-2016
https://dl.acm.org/doi/10.1145/3015022.3015032
Molino PAiello LLops P(2016)Social Question AnsweringACM Transactions on Information Systems10.1145/294806335:1(1-40)Online publication date: 3-Sep-2016
https://dl.acm.org/doi/10.1145/2948063
Zhang XLi ZZhu SLiang W(2016)Detecting Spam and Promoting Campaigns in TwitterACM Transactions on the Web10.1145/284610210:1(1-28)Online publication date: 8-Feb-2016
https://dl.acm.org/doi/10.1145/2846102
Ben Rjab AKharoune MMiklos ZMartin A(2016)Characterization of Experts in Crowdsourcing PlatformsBelief Functions: Theory and Applications10.1007/978-3-319-45559-4_10(97-104)Online publication date: 8-Sep-2016
https://doi.org/10.1007/978-3-319-45559-4_10
Klašnja-Milićević AVesin BIvanović MBudimac ZJain LKlašnja-Milićević AVesin BIvanović MBudimac ZJain L(2016)Folksonomy and Tag-Based Recommender Systems in E-Learning EnvironmentsE-Learning Systems10.1007/978-3-319-41163-7_7(77-112)Online publication date: 20-Jul-2016
https://doi.org/10.1007/978-3-319-41163-7_7
Ginsca APopescu ALupu M(2015)Credibility in Information RetrievalFoundations and Trends in Information Retrieval10.1561/15000000469:5(355-475)Online publication date: 1-Dec-2015
https://dl.acm.org/doi/10.1561/1500000046
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