Abstract
Since current search engines employ link-based ranking algorithms as an important tool to decide a ranking of sites, Web spammers are making a significant effort to manipulate the link structure of the Web, so called, link spamming. Link hijacking is an indispensable technique for link spamming to bring ranking scores from normal sites to target spam sites. In this paper, we propose a link analysis technique for finding link hijacked sites using modified PageRank algorithms. We performed experiments on the large scale Japanese Web archive and evaluated the accuracy of our method. Detection precision of our approach was improved about 25% from a naive approach.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Nakamura, S., Konishi, S., Jatowt, A., Ohshima, H., Kondo, H., Tezuka, T., Oyama, S., Tanaka, K.: Trustworthiness Analysis of Web Search Results. In: 11th European Conference on Research and Advanced Technology for Digital Libraries, Budapest, Hungary (2007)
Ntoulas, A., Najork, M., Manasse, M., Fetterly, D.: Detecting Spam Web pages through Content Analysis. In: 15th International Conference on World Wide Web, Edinburgh, Scotland, UK (2006)
Fetterly, D., Manasse, M., Najork, M.: Spam, Damn Spam, and Statistics: Using Statistical Analysis to Locate Spam Web Pages. In: 7th International Workshop on the Web and Databases, Paris, France (2005)
Page, L., Brin, S., Motwani, R., Winograd, T.: The PageRank citation ranking: Bringing Order to the Web. Technical report, Stanford Digital Library Technologies Project, Stanford University, Stanford, CA, USA (1998)
Gyöngyi, Z., Garcia-Molina, H.: Web Spam Taxonomy. In: 1st International Workshop on Adversarial Information Retrieval on the Web, Chiba, Japan (2005)
Gyöngyi, Z., Garcia-Molina, H.: Link Spam Alliance. In: 31st International Conference on Very large Data Bases, Trondheim, Norway (2005)
Du, Y., Shi, Y., Zhao, X.: Using Spam Farm to Boost PageRank. In: 3rd International Workshop on Adversarial Information Retrieval on the Web, Banff, Alberta, Canada (2007)
Gyöngyi, Z., Garcia-Molina, H., Pedersen, J.: Combating Web spam with TrustRank. In: 30th International Conference on Very Large Data Bases, Toronto, Canada (2004)
Wu, B., Goel, V., Davison, B.D.: Topical TrustRank: Using Topicality to Combat Web Spam. In: 15th International Conference on World Wide Web, Edinburgh, Scotland, UK (2006)
Gyöngyi, Z., Berkhin, P., Garcia-Molina, H., Pedersen, J.: Link Spam Detection Based on Mass Estimation. In: 32nd international conference on Very Large Data Base, Seoul, Korea (2006)
Krishnan, V., Raj, R.: Web Spam Detection with Anti-TrustRank. In: 2nd International Workshop on Adversarial Information Retrieval on the Web. Edinburgh, Scotland, UK (2006)
Benczur, A., Csalogány, K., Sarlós, T., Uher, M.: SpamRank-fully automatic link spam detection. In: 1st International Workshop on Adversarial Information Retrieval on the Web, Chiba, Japan (2005)
Saito, H., Toyoda, M., Kitsuregawa, M., Aihara, K.: A Large-scale Study of Link Spam Detection by Graph Algorithms. In: 3rd International Workshop on Adversarial Information Retrieval on the Web, Banff, Alberta, Canada (2007)
Najork, M., Wiener, J.L.: Breadth-first Crawling Yields High-quality Pages. In: 10th international conference on World Wide Web, Hong Kong (2001)
The Official Google Blog, http://googleblog.blogspot.com/2005/01/preventing-comment-spam.html
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chung, Yj., Toyoda, M., Kitsuregawa, M. (2009). Detecting Link Hijacking by Web Spammers. In: Theeramunkong, T., Kijsirikul, B., Cercone, N., Ho, TB. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2009. Lecture Notes in Computer Science(), vol 5476. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01307-2_32
Download citation
DOI: https://doi.org/10.1007/978-3-642-01307-2_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01306-5
Online ISBN: 978-3-642-01307-2
eBook Packages: Computer ScienceComputer Science (R0)