Spam web page detection using combined content and link features
by Rajendra Kumar Roul; Shubham Rohan Asthana; Gaurav Kumar
International Journal of Data Mining, Modelling and Management (IJDMMM), Vol. 8, No. 3, 2016

Abstract: Web spamming refers to actions that have intentions to mislead search engines by ranking some irrelevant web pages higher in the search results than they deserve. It is thus a roadblock in obtaining high-quality information retrieval from the web. Spam web pages are often littered with irrelevant and meaningless content. Therefore, spam detection methods have been proposed as a solution for web spam in order to minimise the adverse effects of spam web pages. There has been no single defining profile that can encompass all types of spam websites. As such, this makes spam web page detection extremely difficult. In this paper, the proposed technique combines the content and link-based features of web pages to classify them as spam or non-spam. For experimental purpose, WEBSPAM-UK2006 dataset has been used. The results of the proposed approach were compared with the existing approaches and it has been found that the F-measure of the proposed approach outperformed the others.

Online publication date: Mon, 12-Sep-2016

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Data Mining, Modelling and Management (IJDMMM):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email