Using rank propagation and Probabilistic counting for Link-Based Spam Detection

This paper proposes spam detection techniques that only consider the link structure of Web, regardless of page contents, and compute statistics of the links in the vicinity of every Web page applying rank propagation and probabilistic counting over the Web graph.

Abstract

This paper describes a link-based technique for automating the detection of Web spam, that is, pages using deceptive techniques for obtaining an undeservedly high score in search engines. The problem of Web spam is widespread and difficult to solve, mostly due to the large size of the Web that makes many algorithms infeasible in practice. We propose spam detection techniques that only consider the link structure of Web, regardless of page contents. In particular, we compute statistics of the links in the vicinity of every Web page applying rank propagation and probabilistic counting over the Web graph. These statistical features are used to build a classifier that is tested over a large collection of Web link spam. After ten-fold cross-validation, our best classifier can detect about 80 % of the spam hosts with a rate of false positives of 2%. This is competitive with state-of-the-art spam classifiers that use content attributes. 1.