Low cost page quality factors to detect web spam
This addresses the problem of search engine quality degradation due to web spam, but it is incremental as it builds on existing feature-based detection methods.
The paper tackled web spam detection by proposing 32 low-cost quality factors (URL, content, and link features) and developed a neural network classifier using Resilient Back-propagation, achieving good accuracy for real-time application.
Web spam is a big challenge for quality of search engine results. It is very important for search engines to detect web spam accurately. In this paper we present 32 low cost quality factors to classify spam and ham pages on real time basis. These features can be divided in to three categories: (i) URL features, (ii) Content features, and (iii) Link features. We developed a classifier using Resilient Back-propagation learning algorithm of neural network and obtained good accuracy. This classifier can be applied to search engine results on real time because calculation of these features require very little CPU resources.