of their Web pages in the Search Engine Results Pages (SERPs). This study represents an enhancement to previous work in this field. It includes the design and implementation of an online Arabic Web spam detection system, based on algorithms and mathematical foundations, which can detect the Arabic content

goals. This study is a continuation of a series of Arabic Web spam studies conducted by the authors, where this study is dedicated to build the first Arabic content/link Web spam detection system. This Novel system is capable to extract the set of content and link features of Web pages, in order

In this paper, we continue our investigations of “web spam”: the injection of artificially-created pages into the web in order to influence the results from search engines, to drive traffic to certain pages for fun or profit. This paper considers some previously-undescribed techniques

have been investigated extensively. We will see that opinion spam is quite different from Web spam and email spam, and thus requires different detection techniques. Based on the analysis of 5.8 million reviews and 2.14 million reviewers from amazon.com, we show that opinion spam in reviews

Web spam can significantly deteriorate the quality of search engine results. Thus there is a large incentive for commercial search engines to detect spam pages efficiently and accurately. In this paper we present a spam detection system that uses the topology of the Web graph by exploiting the link

resources and creates dissatisfaction among web community. Web Security being a prime challenge for search engines has motivated the researchers in academia and industry to devise new techniques for web spam detection. In this paper we present a comprehensive survey of techniques for detection of web spam

We propose link-based techniques for automating the detection of Web spam, a term referring to pages which use deceptive techniques to obtain undeservedly high scores in search engines. The issue of Web spam is widespread and difficult to solve, mostly due to the large size of the Web which means

then install malware on the victims machines. In this paper we introduce a clustering and classification approach to detect spam web pages in the list of results that are returned by a search engine. Initially, we apply K-nearest neighbor approach for clustering. And then we will apply K-means classification

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We perform a statistical analysis of a large collection of Web pages, focusing on spam detection. We study several metrics such as degree correlations, number of neighbors, rank propagation through links, TrustRank and others to build several automatic web spam classifiers. This paper presents a