Spam detection through link authorization from neighboring nodes

Current link spam techniques aim at manipulating both good and bad pages to boost their desired target page(s) and attract web surfers. The web structure of today includes links from bad to good pages and vice versa as well as pages of same kind. It is widely known that good pages seldom connect to bad ones, hence, spamming is assumed when such connections occur. Therefore, such good pages are penalized. However, such penalization tend to be unfair since every web page has an honest and dishonest part. Besides, several factors such as pages similarity influences the web hyperlinks distribution. Based on this, the paper proposes Link Authorization Model to detect link spam propagation onto neighboring pages. We design metrics with relevant link and content features to compute the angular similarity between connecting good-bad pages. Then based on the angular similarity, we are able to predict page-links as true or false authorization. Hence, for every false authorization detected, the out-going page receives a penalization by a pre-determined threshold. Our results show an average spamicity of 0.77 and a corresponding demotion of 0.60.