The classification power of Web features

    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, Logit-
    Boost, 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
    including the temporal features of eight .uk crawl snapshots that include WEBSPAMUK2007
    as well as the Web Spam Challenge features for the labeled part of ClueWeb09.

    Miklos Erdelyi, Andras A. Benczur. Balint Daroczy, Andras Garzo, Tamas Kiss, David Siklosi
    Internet Mathematics