Link analysis for Web spam detection

Luca Becchetti, Carlos Castillo, Debora Donato, Ricardo Baeza-Yates, Stefano Leonardi

Research output: Contribution to journalArticle

74 Citations (Scopus)

Abstract

We propose link-based techniques for automatic detection of Web spam, a term referring to pages which use deceptive techniques to obtain undeservedly high scores in search engines. The use of Web spam is widespread and difficult to solve, mostly due to the large size of the Web which means that, in practice, many algorithms are infeasible. We perform a statistical analysis of a large collection of Web pages. In particular, we compute statistics of the links in the vicinity of every Web page applying rank propagation and probabilistic counting over the entire Web graph in a scalable way. These statistical features are used to build Web spam classifiers which only consider the link structure of the Web, regardless of page contents. We then present a study of the performance of each of the classifiers alone, as well as their combined performance, by testing them over a large collection of Web link spam. After tenfold cross-validation, our best classifiers have a performance comparable to that of state-of-the-art spam classifiers that use content attributes, but are orthogonal to content-based methods.

Original languageEnglish
Article number2
JournalACM Transactions on the Web
Volume2
Issue number1
DOIs
Publication statusPublished - 1 Feb 2008

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Keywords

  • Adversarial information retrieval
  • Link analysis

ASJC Scopus subject areas

  • Computer Networks and Communications

Cite this

Becchetti, L., Castillo, C., Donato, D., Baeza-Yates, R., & Leonardi, S. (2008). Link analysis for Web spam detection. ACM Transactions on the Web, 2(1), [2]. https://doi.org/10.1145/1326561.1326563