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Web spam detection via commercial intent analysis

Authors:

András Benczúr,

István Bíró,

Károly Csalogány,

Tamás SarlósAuthors Info & Claims

AIRWeb '07: Proceedings of the 3rd international workshop on Adversarial information retrieval on the web

Pages 89 - 92

https://doi.org/10.1145/1244408.1244424

Published: 08 May 2007 Publication History

Abstract

We propose a number of features for Web spam filtering based on the occurrence of keywords that are either of high advertisement value or highly spammed. Our features include popular words from search engine query logs as well as high cost or volume words according to Google AdWords. We also demonstrate the spam filtering power of the Online Commercial Intention (OCI) value assigned to an URL in a Microsoft adCenter Labs Demonstration and the Yahoo! Mindset classification of Web pages as either commercial or non-commercial as well as metrics based on the occurrence of Google ads on the page. We run our tests on the WEBSPAM-UK2006 dataset recently compiled by Castillo et al. as a standard means of measuring the performance of Web spam detection algorithms. Our features improve the classification accuracy of the publicly available WEBSPAM-UK2006 features by 3%.

References

[1]

A. A. Benczúr, K. Csalogány, E. Friedman, D. Fogars, T. Sarlós, M. Uher, and E. Windhager. Searching a small national domain---preliminary report. In Proc. WWW, 2003.

[2]

A. A. Benczúr, K. Csalogány, and T. Sarlós. Link-based similarity search to fight web spam. In Proc. AIRWeb, 2006.

[3]

A. A. Benczúr, K. Csalogány, and T. Sarlós, and M. Uher. SpamRank -- Fully automatic link spam detection. In Proc. AIRWeb, 2005.

[4]

A. Z. Broder. taxonomy of web search. SIGIR Forum, 36(2):3--10, 2002.

Digital Library

[5]

C. Castillo, D. Donato, L. Becchetti, P. Boldi, S. Leonardi, M. Santini, and S. Vigna. A reference collection for web spam. SIGIR Forum, 40(2), 2006.

Digital Library

[6]

C. Castillo, D. Donato, A. Gionis, V. Murdock, and F. Silvestri. Know your neighbors: Web spam detection using the web topology. DELIS Technical report TR-0458, 2006.

[7]

K. Chellapilla and D. M. Chickering. Improving cloaking detection using search query popularity and monetaizability. In Proc. AIRWeb, pages 17--24, 2006.

[8]

H. K. Dai, L. Zhao, Z. Nie, J.-R. Wen, L. Wang, and Y. Li. Detecting online commercial intention (OCI). In Proc. WWW, pages 829--837, 2006.

Digital Library

[9]

I. Drost and T. Scheffer. Thwarting the nigritude ultramarine: Learning to identify link spam. In Proc. ECML, volume 3720 of LNAI, pages 233--243, 2005.

Digital Library

[10]

N. Eiron, K. S. McCurley, and J. A. Tomlin. Ranking the web frontier. In Proc. WWW, pages 309--318, 2004.

Digital Library

[11]

R. Fagin, R. Kumar, K. S. McCurley, J. Novak, D. Sivakumar, J. A. Tomlin, and D. P. Williamson. Searching the workplace web. In Proc. WWW, 2003.

Digital Library

[12]

D. Fetterly, M. Manasse, and M. Najork. Spam, damn spam, and statistics - Using statistical analysis to locate spam web pages. In Proc. WebDB, 2004.

Digital Library

[13]

Z. Gyöngyi and H. Garcia-Molina. Spam: It's not just for inboxes anymore. IEEE Computer Magazine, 38(10):28--34, 2005.

Digital Library

[14]

Z. Gyöngyi and H. Garcia-Molina. Web spam taxonomy. In Proc. AIRWeb, 2005.

[15]

Z. Gyöngyi, H. Garcia-Molina, and J. Pedersen. Combating web spam with Trust Rank. In Proc. VLDB, pages 576--587, 2004.

Digital Library

[16]

M. R. Henzinger, R. Motwani, and C. Silverstein. Challenges in web search engines. SIGIR Forum, 36(2):11--22, 2002.

Digital Library

[17]

A. Ntoulas, M. Najork, M. Manasse, and D. Fetterly. Detecting spam web pages through content analysis. In Proc. WWW, pages 83--92, 2006.

Digital Library

[18]

Y.-M. Wang, M. Ma, Y. Niu, and H. Chen. Spam double-funnel: Connecting web spammers with advertisers. In Proc. WWW, 2007.

Digital Library

[19]

I. H. Witten and E. Frank. Data Mining: Practical Machine Learning Tools and Techniques, Second edition. Morgan Kaufmann, 2005.

Digital Library

[20]

B. Wu, V. Goel, and B. D. Davison. Propagating trust and distrust to demote web spam. In Workshop on Models of Trust for the Web, 2006.

Cited By

Prashant Ankalkoti (2022)A Study on Types, Classification and Mechanism for Optimization in Search EngineInternational Journal of Advanced Research in Science, Communication and Technology10.48175/IJARSCT-3260(278-284)Online publication date: 23-Apr-2022
https://doi.org/10.48175/IJARSCT-3260
Li Y(2022)Towards Forecasting Internet Financial Frauds based on Advertising2022 8th International Conference on Big Data and Information Analytics (BigDIA)10.1109/BigDIA56350.2022.9874049(5-11)Online publication date: 24-Aug-2022
https://doi.org/10.1109/BigDIA56350.2022.9874049
Shahzad ANawi NRehman MKhan A(2021)An Improved Framework for Content- and Link-Based Web-Spam Detection: A Combined ApproachComplexity10.1155/2021/66257392021(1-18)Online publication date: 15-Nov-2021
https://doi.org/10.1155/2021/6625739
Show More Cited By

Index Terms

Web spam detection via commercial intent analysis
1. Applied computing
  1. Document management and text processing
    1. Document capture
2. Information systems
  1. Information retrieval

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Published In

cover image ACM Other conferences

AIRWeb '07: Proceedings of the 3rd international workshop on Adversarial information retrieval on the web

May 2007

98 pages

ISBN:9781595937322

DOI:10.1145/1244408

Conference Chairs:
Carlos Castillo
Yahoo! Research
,
Kumar Chellapilla
Microsoft Live Labs
,
Brian D. Davison
Lehigh University

Copyright © 2007 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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 May 2007

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AIRWeb'07

AIRWeb'07: AIRWeb'07, Third International Workshop on Adversarial Information Retrieval on the Web

May 8, 2007

Alberta, Banff, Canada

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Cited By

Prashant Ankalkoti (2022)A Study on Types, Classification and Mechanism for Optimization in Search EngineInternational Journal of Advanced Research in Science, Communication and Technology10.48175/IJARSCT-3260(278-284)Online publication date: 23-Apr-2022
https://doi.org/10.48175/IJARSCT-3260
Li Y(2022)Towards Forecasting Internet Financial Frauds based on Advertising2022 8th International Conference on Big Data and Information Analytics (BigDIA)10.1109/BigDIA56350.2022.9874049(5-11)Online publication date: 24-Aug-2022
https://doi.org/10.1109/BigDIA56350.2022.9874049
Shahzad ANawi NRehman MKhan A(2021)An Improved Framework for Content- and Link-Based Web-Spam Detection: A Combined ApproachComplexity10.1155/2021/66257392021(1-18)Online publication date: 15-Nov-2021
https://doi.org/10.1155/2021/6625739
Gupta AKumar RTiwari M(2020)Taxonomy of Link Based web Spammers using Mining Optimized PageRank Algorithm for e-Governance2020 International Conference on Intelligent Engineering and Management (ICIEM)10.1109/ICIEM48762.2020.9160317(155-159)Online publication date: Jun-2020
https://doi.org/10.1109/ICIEM48762.2020.9160317
Danilov ASalekhova LGrigorieva KZaripova RZinnurov T(2020)The application of statistical methods in the development of Cyrillic-Latin converter for Tatar language2020 13th International Conference on Developments in eSystems Engineering (DeSE)10.1109/DeSE51703.2020.9450742(293-298)Online publication date: 14-Dec-2020
https://doi.org/10.1109/DeSE51703.2020.9450742
Adekotujo ALee JEnikuomehin AMazzara MAribisala S(2019)Bi-lingual Intent Classification of Twitter Posts: A RoadmapProceedings of 6th International Conference in Software Engineering for Defence Applications10.1007/978-3-030-14687-0_1(1-9)Online publication date: 19-Mar-2019
https://doi.org/10.1007/978-3-030-14687-0_1
Lv YLiu JChen HMi JLiu MZheng Q(2017)Opinioned Post Detection in Sina WeiboIEEE Access10.1109/ACCESS.2017.26792275(7263-7271)Online publication date: 2017
https://doi.org/10.1109/ACCESS.2017.2679227
ROUL RASTHANA SSHAH MPARIKH D(2016)Detecting spam web pages using content and link-based techniquesSadhana10.1007/s12046-015-0460-941:2(193-202)Online publication date: 10-Mar-2016
https://doi.org/10.1007/s12046-015-0460-9
Hollerit BKröll MStrohmaier MSchwabe DAlmeida VGlaser HBaeza-Yates RMoon S(2013)Towards linking buyers and sellersProceedings of the 22nd International Conference on World Wide Web10.1145/2487788.2488009(629-632)Online publication date: 13-May-2013
https://dl.acm.org/doi/10.1145/2487788.2488009
Torabi ATaghipour KKhadivi S(2013)Web Spam Detection: New Approach with Hidden Markov ModelsInformation Retrieval Technology10.1007/978-3-642-45068-6_21(239-250)Online publication date: 2013
https://doi.org/10.1007/978-3-642-45068-6_21
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