Spam pages on the web use various techniques to artificially achieve high rankings in search engine results. Human experts can do a good job of identifying spam pages and pages whose information is of dubious quality, but it is practically infeasible to use human effort for a large number of pages. Similar to the approach in , we propose a method of selecting a seed set of pages to be evaluated by a human. We then use the link structure of the web and the manually labeled seed set, to detect other spam pages. Our experiments on the WebGraph dataset  show that our approach is very effective at detecting spam pages from a small seed set and achieves higher precision of spam page detection than the Trust Rank algorithm, apart from detecting pages with higher pageranks, on an average. 1.