Abstract
Search engine is critical in people’s daily life because it determines the information quality people obtain through searching. Fierce competition for the ranking in search engines is not conducive to both users and search engines. Existing research mainly studies the content and links of websites. However, none of these techniques focused on semantic analysis of link and anchor text for detection. In this paper, we propose a web spam detection method by extracting novel feature sets from the homepage source code and choosing the random forest (RF) as the classifier. The novel feature sets are extracted from the homepage’s links, hypertext markup language (HTML) structure, and semantic similarity of content. We conduct experiments on the WEBSPAM-UK2007 and UK-2011 dataset using a five-fold cross-validation method. Besides, we design three sets of experiments to evaluate the performance of the proposed method. The proposed method with novel feature sets is compared with different indicators and has better performance than other methods with a precision of 0.929 and a recall of 0.930. Experiment results show that the proposed model could effectively detect web spam.
Highlights
With the rapid development of the network, web applications are becoming more and more popular in the recent years, among which search engines are one of the most common web tools for people to gain information every day [1]
Spammers design pages delicately to improve rankings as most users only access the first page of search results. ere has been a brief definition of web spamming in the literature [5]; shortly speaking, web spamming is a black-hat search engine optimization (SEO) that deceive search engines to increase the ranking of a page in search engine results. ese web pages are called web spam
Trees of random forest (RF) algorithm are independent during the training process. e final result is obtained by voting of all trees
Summary
With the rapid development of the network, web applications are becoming more and more popular in the recent years, among which search engines are one of the most common web tools for people to gain information every day [1]. Ere has been a brief definition of web spamming in the literature [5]; shortly speaking, web spamming is a black-hat search engine optimization (SEO) that deceive search engines to increase the ranking of a page in search engine results. Spammers try to deceive search engines and attract end users to click on web spam sites. Ey reduce the effectiveness and efficiency of search engine results since web spam pages take much time to process but may be full of malicious content and links. Search engine companies have utilized various methods to counter spam [7], it is still a challenge to prevent the increase of blackhat SEO technology and the growth of spam pages nowadays
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