Title
WSF2: a novel framework for filtering web spam
Abstract
AbstractOver the last years, research on web spam filtering has gained interest from both academia and industry. In this context, although there are a good number of successful antispam techniques available (i.e., content-based, link-based, and hiding), an adequate combination of different algorithms supported by an advanced web spam filtering platform would offer more promising results. To this end, we propose the WSF2 framework, a new platform particularly suitable for filtering spam content on web pages. Currently, our framework allows the easy combination of different filtering techniques including, but not limited to, regular expressions and well-known classifiers (i.e., Naïve Bayes, Support Vector Machines, and C5.0). Applying our WSF2 framework over the publicly available WEBSPAM-UK2007 corpus, we have been able to demonstrate that a simple combination of different techniques is able to improve the accuracy of single classifiers on web spam detection. As a result, we conclude that the proposed filtering platform is a powerful tool for boosting applied research in this area.
Year
DOI
Venue
2016
10.1155/2016/6091385
Periodicals
Field
DocType
Volume
Data mining,Regular expression,Web page,Naive Bayes classifier,Computer science,Support vector machine,Filter (signal processing),Boosting (machine learning),Applied research,Spamdexing
Journal
2016
Issue
ISSN
Citations 
1
1058-9244
4
PageRank 
References 
Authors
0.41
30
6
Name
Order
Citations
PageRank
J. Fdez-Glez1263.28
David Ruano-Ordás2949.32
Rosalia Laza313114.52
José Ramon Méndez425417.69
Reyes Pavón5578.08
Florentino Fdez-Riverola646457.16