Improving spamdexing detection via a two-stage classification strategy

Abstract

Spamdexing is any of various methods to manipulate the relevancy or prominence of resources indexed by a search engine, usually in a manner inconsistent with the purpose of the indexing system. Combating Spamdexing has become one of the top challenges for web search. Machine learning based methods have shown their superiority for being easy to adapt to newly developed spam techniques. In this paper, we propose a two-stage classification strategy to detect web spam, which is based on the predicted spamicity of learning algorithms and hyperlink propagation. Preliminary experiments on standard WEBSPAM-UK2006 benchmark show that the two-stage strategy is reasonable and effective. © 2008 Springer-Verlag Berlin Heidelberg.