Article

Spam double-funnel: connecting web spammers with advertisers

Authors:

Hao ChenAuthors Info & Claims

WWW '07: Proceedings of the 16th international conference on World Wide Web

Pages 291 - 300

https://doi.org/10.1145/1242572.1242612

Published: 08 May 2007 Publication History

Abstract

Spammers use questionable search engine optimization (SEO) techniques to promote their spam links into top search results. In this paper, we focus on one prevalent type of spam - redirection spam - where one can identify spam pages by the third-party domains that these pages redirect traffic to. We propose a five-layer, double-funnel model for describing end-to-end redirection spam, present a methodology for analyzing the layers, and identify prominent domains on each layer using two sets of commercial keywords. one targeting spammers and the other targeting advertisers. The methodology and findings are useful for search engines to strengthen their ranking algorithms against spam, for legitimate website owners to locate and remove spam doorway pages, and for legitimate advertisers to identify unscrupulous syndicators who serve ads on spam pages.

References

[1]

Adali, S., Liu, T., and Magdon-Ismail, M. Optimal Link Bombs are Uncoordinated. In the 1st International Workshop on Adversarial Information Retrieval on the Web (AIRWeb), May 2005.

[2]

Baeza-Yates, R, Castillo, C., and Lopez, V. Pagerank Increase Under Different Collusion Topologies. In the 1st International Workshop on Adversarial Information Retrieval on the Web (AIRWeb), May 2005.

[3]

Becchetti, L., Castillo, C., Donato, D., Leonardi, S., Baeza-Yates, R. Link-based Characterization and Detection of Web Spam. In the 2nd International Workshop on Adversarial Information Retrieval on the Web (AIRWeb), August 2006.

[4]

Benczur, A., Csalogany, K., Sarlos, T., and Uher, M. SpamRank -- Fully Automatic Link Spam Detection. In the 1st International Workshop on Adversarial Information Retrieval on the Web (AIRWeb), May 2005.

[5]

Chellapilla, K. and Chickering, D.M. Improving Cloaking Detection Using Search Query Popularity and Monetizability. In the 2nd International Workshop on Adversarial Information Retrieval on the Web (AIRWeb), August 2006.

[6]

da Costa Carvalho, A. L., Chirita, P., de Moura, E. S., Calado, P., and Nejdl, W. Site Level Noise Removal for Search Engines. In Proc. of International World Wide Web Conference (WWW). May, 2006.

Digital Library

[7]

Fetterly, D., Manasse, M., and Najork, M. Spam, Damn Spam, and Statistics: Using Statistical Analysis to Locate Spam Web Pages. In Proc of the 7th International Workshop on the Web and Databases. pp. 1--6, 2004.

Digital Library

[8]

Gyongyi, Z. and Garcia-Molina, H. Web Spam Taxonomy. In the 1st International Workshop on Adversarial Information Retrieval on the Web (AIRWeb), 2005.

[9]

Jansen, B.J. Adversarial Informaton Retrieval Aspects of Sponsored Search. In the 2nd International Workshop on Adversarial Information Retrieval on the Web (AIRWeb), 2006.

[10]

Jarvelin, K. and Kekalainen, J. IR Evaluation Methods for Retrieving Highly Relevant Documents. In Proc. ACM SIGIR Conference on R&D in Information Retrieval, 2000.

Digital Library

[11]

Kolari, P., Tim Finin, T., and Joshi, A. SVMs for the Blogosphere: Blog Identification and Splog Detection. In AAAI Spring Symposium on Computational Approaches to Analysing Weblogs, March 2006.

[12]

Krishnan, V. and Raj, R. Web Spam Detection and Anti-Trust Rank. In the 2nd International Workshop on Adversarial Information Retrieval on the Web (AIRWeb), August 2006.

[13]

Metaxas, P. and DeStephano, J. Web Spam, Propaganda and Trust. In the 1st International Workshop on Adversarial Information Retrieval on the Web (AIRWeb), May 2005.

[14]

Mishne, G., Carmel, D., and Lempel, R. Blocking Blog Spam with Language Model Disagreement. In the 1st International Workshop on Adversarial Information Retrieval on the Web (AIRWeb), May 2005.

[15]

Niu, Y., Wang, Y. M., Chen, H., Ma, M., and Hsu, F. A Quantitative Study of Forum Spamming Using Context-based Analysis. In Proc. Network and Distributed System Security (NDSS) Symposium, February 2007.

[16]

Ntoulas, A., Najork, M., Manasse, M., and Fetterly, D. Detecting Spam Web Pages through Content Analysis. In Proc. International World Wide Web Conference (WWW), May 2006.

Digital Library

[17]

Sarukkai, R.R. How Much is a Keyword Worth? In Proc. International World Wide Web Conference, (WWW), May 2005.

Digital Library

[18]

Urvoy, T., Lavernge, T., Filoche, P. Tracking Web Spam with Hidden Style Similarity. In the 2nd International Workshop on Adversarial Information Retrieval on the Web (AIRWeb), August 2006.

[19]

Wang, Y. M., Beck, D., Jiang, X., Roussev, R., Verbowski, C., Chen, S., and King, S. Automated Web Patrol with Strider HoneyMonkeys: Finding Web Sites That Exploit Browser Vulnerabilities. In Proc. Network and Distributed System Security (NDSS) Symposium, February 2006.

[20]

Wang, Y. M., Beck, D., Wang, J., Verbowski, C., and Daniels, B. Strider Typo-Patrol: Discovery and Analysis of Systematic Typo-Squatting. In Proc. 2nd Workshop on Steps to Reducing Unwanted Traffic on the Internet (SRUTI), July 2006.

Digital Library

[21]

Wang, Y. M. and Ma, M. Strider Search Ranger: Towards an Autonomic Anti-Spam Search Engine. Microsoft Research Technical Report, MSR-TR-2006-174, December 2006.

[22]

Wang, Y. M. and Ma, M. Detecting Stealth Web Pages That Use Click-Through Cloaking. Microsoft Research Technical Report, MSR-TR-2006-178, December 2006.

[23]

Wu, B. and Davison, B.D. Cloaking and Redirection: A Preliminary Study. In the 1st International Workshop on Adversarial Information Retrieval on the Web (AIRWeb), 2005.

[24]

Wu, B., and Davison, B. D. Identifying Link Farm Pages. In Proc. International World Wide Web Conference (WWW), 2005.

Digital Library

[25]

Wu, B. and Davison, B.D. Detecting Semantic Cloaking on the Web. In Proc. International World Wide Web Conference (WWW), August 2006.

Digital Library

[26]

Wu, B., Goel, V., Davison, B.D. Propagating Trust and Distrust to Demote Web Spam. In Proc. Models of Trust for the Web Workshop (MTW), International World Wide Web Conference, 2006.

[27]

Fiddler HTTP Proxy, http://www.fiddlertool.com/

[28]

Fighting Splogs, http://fightsplog.blogspot.com/

[29]

The Google AdSense Program, http://google.com/adsense

[30]

Network Whois records, http://whois.domaintools.com/ 66.230.138.211 and http://whois.domaintools.com/64.111.214.154

[31]

Screenshots of sample redirection spam pages, http://research.microsoft.com/SearchRanger/Redirection-spam_3_types.htm

[32]

Screenshots of sample click-through analyses, http://research.microsoft.com/SearchRanger/Spam_ads_click-through_analysis.htm

Cited By

Crichton KChristin NCranor L(2021)How Do Home Computer Users Browse the Web?ACM Transactions on the Web10.1145/347334316:1(1-27)Online publication date: 28-Sep-2021
https://dl.acm.org/doi/10.1145/3473343
Elyashar AUziel SParadise APuzis R(2020)The Chameleon Attack: Manipulating Content Display in Online Social MediaProceedings of The Web Conference 202010.1145/3366423.3380165(848-859)Online publication date: 20-Apr-2020
https://dl.acm.org/doi/10.1145/3366423.3380165
Najork M(2018)Web Spam DetectionEncyclopedia of Database Systems10.1007/978-1-4614-8265-9_465(4677-4681)Online publication date: 7-Dec-2018
https://doi.org/10.1007/978-1-4614-8265-9_465
Show More Cited By

Index Terms

Spam double-funnel: connecting web spammers with advertisers
1. Information systems
  1. World Wide Web

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Published In

cover image ACM Conferences

WWW '07: Proceedings of the 16th international conference on World Wide Web

May 2007

1382 pages

ISBN:9781595936547

DOI:10.1145/1242572

General Chairs:
Carey Williamson
University of Calgary, Canada
,
Mary Ellen Zurko
IBM, USA
,
Program Chairs:
Peter Patel-Schneider
Bell Labs Research, USA
,
Prashant Shenoy
University of Massachusetts at Amherst, USA

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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Publication History

Published: 08 May 2007

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WWW'07: 16th International World Wide Web Conference

May 8 - 12, 2007

Alberta, Banff, Canada

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

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Cited By

Crichton KChristin NCranor L(2021)How Do Home Computer Users Browse the Web?ACM Transactions on the Web10.1145/347334316:1(1-27)Online publication date: 28-Sep-2021
https://dl.acm.org/doi/10.1145/3473343
Elyashar AUziel SParadise APuzis R(2020)The Chameleon Attack: Manipulating Content Display in Online Social MediaProceedings of The Web Conference 202010.1145/3366423.3380165(848-859)Online publication date: 20-Apr-2020
https://dl.acm.org/doi/10.1145/3366423.3380165
Najork M(2018)Web Spam DetectionEncyclopedia of Database Systems10.1007/978-1-4614-8265-9_465(4677-4681)Online publication date: 7-Dec-2018
https://doi.org/10.1007/978-1-4614-8265-9_465
Hans KAhuja LMuttoo S(2017)An adaptive neuro-fuzzy inference system for detecting redirection spam2017 6th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)10.1109/ICRITO.2017.8342490(559-564)Online publication date: Sep-2017
https://doi.org/10.1109/ICRITO.2017.8342490
Goswami AKumar A(2017)Challenges in the Analysis of Online Social NetworksWireless Personal Communications: An International Journal10.1007/s11277-017-4712-397:3(4015-4061)Online publication date: 1-Dec-2017
https://dl.acm.org/doi/10.1007/s11277-017-4712-3
Hans KAhuja LMuttoo S(2017)Detecting redirection spam using multilayer perceptron neural networkSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-017-2531-921:13(3803-3814)Online publication date: 1-Jul-2017
https://dl.acm.org/doi/10.1007/s00500-017-2531-9
Molla ABiadgie YSohn K(2017)Detecting Negative Deceptive Opinion from TweetsMobile and Wireless Technologies 201710.1007/978-981-10-5281-1_36(329-339)Online publication date: 17-Jun-2017
https://doi.org/10.1007/978-981-10-5281-1_36
Najork M(2017)Web Spam DetectionEncyclopedia of Database Systems10.1007/978-1-4899-7993-3_465-3(1-5)Online publication date: 11-Feb-2017
https://doi.org/10.1007/978-1-4899-7993-3_465-3
Hans KAhuja LMuttoo S(2016)A fuzzy logic approach for detecting redirection spamInternational Journal of Electronic Security and Digital Forensics10.1504/IJESDF.2016.0774358:3(191-204)Online publication date: 1-Jan-2016
https://dl.acm.org/doi/10.1504/IJESDF.2016.077435
Fdez-Glez JRuano-Ordás DLaza RMéndez JPavón RFdez-Riverola F(2016)WSF2Scientific Programming10.1155/2016/60913852016(1-1)Online publication date: 1-Jan-2016
https://dl.acm.org/doi/10.1155/2016/6091385
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