A systematic framework to discover pattern for web spam classification
This addresses the problem of web spam for search engine users, but it appears incremental as it builds on existing methods like CHAID and KMP.
The authors tackled web spam classification by proposing a systematic framework combining CHAID algorithm and a modified KMP string matching algorithm for feature extraction and analysis, achieving evaluation on a dataset of Alexa Top 500 Global Sites and Bing search engine results with 500 queries.
Web spam is a big problem for search engine users in World Wide Web. They use deceptive techniques to achieve high rankings. Although many researchers have presented the different approach for classification and web spam detection still it is an open issue in computer science. Analyzing and evaluating these websites can be an effective step for discovering and categorizing the features of these websites. There are several methods and algorithms for detecting those websites, such as decision tree algorithm. In this paper, we present a systematic framework based on CHAID algorithm and a modified string matching algorithm (KMP) for extract features and analysis of these websites. We evaluated our model and other methods with a dataset of Alexa Top 500 Global Sites and Bing search engine results in 500 queries.