Web spam pages use various techniques to achieve higher-than-deserved rankings in a search engine's results. While human experts can identify spam, it is too expensive to manually evaluate a large number of pages. Instead, we propose techniques to semi-automatically separate reputable, good pages from spam. We first select a small set of seed pages to be evaluated by an expert. Once we manually identify the reputable seed pages, we use the link structure of the web to discover other pages that are likely to be good. In this paper we discuss possible ways to implement the seed selection and the discovery of good pages. We present results of experiments run on the World Wide Web indexed by AltaVista and evaluate the performance of our techniques. Our results show that we can effectively filter out spam from a significant fraction of the web, based on a good seed set of less than 200 sites.
Gyongyi et al. REF present a semi-automatic algorithm, TrustRank, which uses the link structure of the Web to discover documents that are likely to be legitimate with respect to a small set of (seed) legitimate documents that were manually identified by experts.
Combating web spam with TrustRank
Proceedings of VLDB
Computer Science