Link spam target detection using page farms

Abstract

Currently, most popular Web search engines adopt some link-based ranking methods such as PageRank. Driven by the huge potential benefit of improving rankings of Web pages, many tricks have been attempted to boost page rankings. The most common way, which is known as link spam, is to make up some artificially designed link structures. Detecting link spam effectively is a big challenge. In this article, we develop novel and effective detection methods for link spam target pages using page farms. The essential idea is intuitive: whether a page is the beneficiary of link spam is reflected by how it collects its PageRank score. Technically, how a target page collects its PageRank score is modeled by a page farm, which consists of pages contributing a major portion of the PageRank score of the target page. We propose two spamicity measures based on page farms. They can be used as an effective measure to check whether the pages are link spam target pages. An empirical study using a newly available real dataset strongly suggests that our method is effective. It outperforms the state-of-the-art methods like SpamRank and SpamMass in both precision and recall.