Web spam, which refers to any deliberate actions bringing to selected web pages an unjustifiable favorable relevance or importance, is one of the major obstacles for high quality information retrieval on the web. Most of the existing web spam detection methods are supervised that require a large and representative training set of web pages. Moreover, they often assume some global information such as a large web graph and snapshots of a large collection of web pages. However, in many situations such assumptions may not hold. In this paper, we study the problem of unsupervised web spam detection. We introduce the notion of spamicity to measure how likely a page is spam. Spamicity is a more flexible and user-controllable measure than the traditional supervised classification methods. We propose efficient online link spam and term spam detection methods using spamicity. Our methods do not need training and are cost effective. A real data set is used to evaluate the effectiveness and the efficiency of our methods.
A real data set is used to evaluate the effectiveness and the efficiency REF .
A spamicity approach to web spam detection
In Proceedings of the 2008 SIAM International Conference on Data Mining (SDM’08
Computer Science