NLSDF FOR BOOSTING THE RECITAL OF WEB SPAMDEXING CLASSIFICATION

Abstract

Spamdexing is the art of black hat SEO. Features which are more influential for high rank and visibility are manipulated for the SEO task. The motivation behind the work is utilizing the state of the art Website optimization features to enhance the performance of spamdexing detection. Features which play a focal role in current SEO strategies show a significant deviation for spam and non spam samples. This paper proposes 44 features named as NLSDF (New Link Spamdexing Detection Features). Social media creates an impact in search engine results ranking. Features pertaining to the social media were incorporated with the NLSDF features to boost the recital of the spamdexing classification. The NLSDF features with 44 attributes along with 5 social media features boost the classification performance of the WEBSPAM-UK 2007 dataset. The one tailed paired t-test with 95% confidence, performed on the AUC values of the learning models shows significance of the NLSDF.

Keywords

• Web Spam
• Search Engine
• SVM
• Decision Table
• HMM