Thwarting the Nigritude Ultramarine: Learning to Identify Link Spam

Abstract

AbstractThe page rank of a commercial web site has an enormous economic impact because it directly influences the number of potential customers that find the site as a highly ranked search engine result. Link spamming – inflating the page rank of a target page by artificially creating many referring pages – has therefore become a common practice. In order to maintain the quality of their search results, search engine providers try to oppose efforts that decorrelate page rank and relevance and maintain blacklists of spamming pages while spammers, at the same time, try to camouflage their spam pages. We formulate the problem of identifying link spam and discuss a methodology for generating training data. Experiments reveal the effectiveness of classes of intrinsic and relational attributes and shed light on the robustness of classifiers against obfuscation of attributes by an adversarial spammer. We identify open research problems related to web spam.KeywordsSearch EngineRecursive Feature EliminationPage RankOpen Research ProblemTarget PageThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.