Link-Based Characterization and Detection of Web Spam
Luca Becchetti, Carlos Castillo, Debora Donato, Stefano Leonardi, Ricardo A. Baeza-Yates · 2006
We perform a statistical analysis of a large collection of Web pages, focusing on spam detection. We study several metrics such as degree correlations, number of neighbors, rank propagation through links, TrustRank and others to build several automatic web spam classifiers. This paper presents a study of the performance of each of these classifiers alone, as well as their combined performance. Using this approach we are able to detect 80.4% of the Web spam in our sample, with only 1.1% of false positives.