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User behavior oriented web spam detection

Authors:

Yiqun Liu,

Min Zhang,

Shaoping Ma,

Liyun RuAuthors Info & Claims

WWW '08: Proceedings of the 17th international conference on World Wide Web

Pages 1039 - 1040

https://doi.org/10.1145/1367497.1367645

Published: 21 April 2008 Publication History

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Abstract

Combating Web spam has become one of the top challenges for Web search engines. State-of-the-art spam detection techniques are usually designed for specific known types of Web spam and are incapable and inefficient for recently-appeared spam. With user behavior analyses into Web access logs, we propose a spam page detection algorithm based on Bayes learning. Preliminary experiments on Web access data collected by a commercial Web site (containing over 2.74 billion user clicks in 2 months) show the effectiveness of the proposed detection framework and algorithm.

References

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Gyongyi, Zoltan; Garcia-Molina, Hector; Pedersen, Jan. Combating Web Spam with TrustRank, Proceedings of the 30th International Conference on Very Large Data Bases.

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Cited By

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Manaskasemsak BRungsawang A(2015)Web spam detection using trust and distrust-based ant colony optimization learningInternational Journal of Web Information Systems10.1108/IJWIS-12-2014-004711:2(142-161)Online publication date: 15-Jun-2015
https://doi.org/10.1108/IJWIS-12-2014-0047
Wang DIrani DPu C(2014)SPADE: a social-spam analytics and detection frameworkSocial Network Analysis and Mining10.1007/s13278-014-0189-14:1Online publication date: 10-Apr-2014
https://doi.org/10.1007/s13278-014-0189-1
Manaskasemsak BJiarpakdee JRungsawang A(2014)Adaptive Learning Ant Colony Optimization for Web Spam DetectionProceedings of the 14th International Conference on Computational Science and Its Applications ICCSA 2014 - Volume 858410.1007/978-3-319-09153-2_48(642-653)Online publication date: 30-Jun-2014
https://dl.acm.org/doi/10.1007/978-3-319-09153-2_48
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Index Terms

User behavior oriented web spam detection
1. Information systems
  1. Information retrieval
    1. Evaluation of retrieval results
    2. Information retrieval query processing

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Information

Published In

WWW '08: Proceedings of the 17th international conference on World Wide Web

April 2008

1326 pages

ISBN:9781605580852

DOI:10.1145/1367497

General Chairs:
Jinpeng Huai
Beihang University, China
,
Robin Chen
AT&T Labs, USA
,
Hsiao-Wuen Hon
Microsoft Research Asia, China
,
Yunhao Liu
HK University of Science and Technology, Hong Kong
,
Program Chairs:
Wei-Ying Ma
Microsoft Research Asia, China
,
Andrew Tomkins
Yahoo! Research, USA
,
Xiaodong Zhang
The Ohio State University, USA

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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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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Association for Computing Machinery

New York, NY, United States

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Published: 21 April 2008

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WWW '08: The 17th International World Wide Web Conference

April 21 - 25, 2008

Beijing, China

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Manaskasemsak BRungsawang A(2015)Web spam detection using trust and distrust-based ant colony optimization learningInternational Journal of Web Information Systems10.1108/IJWIS-12-2014-004711:2(142-161)Online publication date: 15-Jun-2015
https://doi.org/10.1108/IJWIS-12-2014-0047
Wang DIrani DPu C(2014)SPADE: a social-spam analytics and detection frameworkSocial Network Analysis and Mining10.1007/s13278-014-0189-14:1Online publication date: 10-Apr-2014
https://doi.org/10.1007/s13278-014-0189-1
Manaskasemsak BJiarpakdee JRungsawang A(2014)Adaptive Learning Ant Colony Optimization for Web Spam DetectionProceedings of the 14th International Conference on Computational Science and Its Applications ICCSA 2014 - Volume 858410.1007/978-3-319-09153-2_48(642-653)Online publication date: 30-Jun-2014
https://dl.acm.org/doi/10.1007/978-3-319-09153-2_48
Pevtsov SVolkov SSchwabe DAlmeida VGlaser HBaeza-Yates RMoon S(2013)Russian web spam evolutionProceedings of the 22nd International Conference on World Wide Web10.1145/2487788.2488135(1137-1140)Online publication date: 13-May-2013
https://dl.acm.org/doi/10.1145/2487788.2488135
Chen KZhu PXiong Y(2013)Mining Spam Accounts with User InfluenceProceedings of the 2013 International Conference on Information Science and Cloud Computing Companion10.1109/ISCC-C.2013.85(167-173)Online publication date: 7-Dec-2013
https://dl.acm.org/doi/10.1109/ISCC-C.2013.85
Chen KChen LZhu PXiong Y(2013)Unveil the Spams in WeiboProceedings of the 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing10.1109/GreenCom-iThings-CPSCom.2013.159(916-922)Online publication date: 20-Aug-2013
https://dl.acm.org/doi/10.1109/GreenCom-iThings-CPSCom.2013.159
Spirin NHan J(2012)Survey on web spam detectionACM SIGKDD Explorations Newsletter10.1145/2207243.220725213:2(50-64)Online publication date: 1-May-2012
https://dl.acm.org/doi/10.1145/2207243.2207252
Liu YChen FKong WYu HZhang MMa SRu L(2012)Identifying Web Spam with the Wisdom of the CrowdsACM Transactions on the Web10.1145/2109205.21092076:1(1-30)Online publication date: 1-Mar-2012
https://dl.acm.org/doi/10.1145/2109205.2109207
Shen YMartin T(2010)Intelligent user search behaviour knowledge discoveryInternational Conference on Fuzzy Systems10.1109/FUZZY.2010.5584867(1-8)Online publication date: Jul-2010
https://doi.org/10.1109/FUZZY.2010.5584867
Soledad Pera MNg Y(2009)A structural, content‐similarity measure for detecting spam documents on the webInternational Journal of Web Information Systems10.1108/174400809110062075:4(431-464)Online publication date: 20-Nov-2009
https://doi.org/10.1108/17440080911006207

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Identifying web spam with user behavior analysis

Fighting against web spam: a novel propagation method based on click-through data

Identifying Web Spam with the Wisdom of the Crowds

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