The Classification Power of Web Features

Abstract

In this article we give a comprehensive overview of features devised for web spam detection and investigate how much various classes, some requiring very high computational effort, add to the classification accuracy.

• We collect and handle a large number of features based on recent advances in web spam filtering, including temporal ones; in particular, we analyze the strength and sensitivity of linkage change.

• We propose new, temporal link-similarity-based features and show how to compute them efficiently on large graphs.

• We show that machine learning techniques, including ensemble selection, LogitBoost, and random forest significantly improve accuracy.

• We conclude that, with appropriate learning techniques, a simple and computationally inexpensive feature subset outperforms all previous results published so far on our dataset and can be further improved only slightly by computationally expensive features.

• We test our method on three major publicly available datasets: the Web Spam Challenge 2008 dataset WEBSPAM-UK2007, the ECML/PKDD Discovery Challenge dataset DC2010, and the Waterloo Spam Rankings for ClueWeb09.

Our classifier ensemble sets the strongest classification benchmark compared to participants of the Web Spam and ECML/PKDD Discovery Challenges as well as the TREC Web track.

To foster research in the area, we make several feature sets and source codes public,¹

https://datamining.sztaki.hu/en/download/web-spam-resources

including the temporal features of eight .uk crawl snapshots that include WEBSPAM-UK2007 as well as the Web Spam Challenge features for the labeled part of ClueWeb09.

Notes

https://datamining.sztaki.hu/en/download/web-spam-resources

The dataset can be downloaded from: http://law.di.unimi.it/datasets.php

http://icu-project.org/

http://www.lextek.com/manuals/onix/stopwords1.html

http://code.google.com/p/fast-random-forest/.

The exact classifier model specification files used for Weka and the data files used for the experiments are available upon request from the authors.

A summary is available as part of our data release at https://dms.sztaki.hu/sites/dms.sztaki.hu/files/download/2013/enpt-queries.txt.gz.

The temporal feature data used in our research is available at: https://datamining.sztaki.hu/en/download/web-spam-resources