  Web content quality measurement is crucial to various web content processing
applications. This paper will explore multi-scale features which may affect the
quality of a host, and develop automatic statistical methods to evaluate the
Web content quality. The extracted properties include statistical content
features, page and host level link features and TFIDF features. The experiments
on ECML/PKDD 2010 Discovery Challenge data set show that the algorithm is
effective and feasible for the quality tasks of multiple languages, and the
multi-scale features have different identification ability and provide good
complement to each other for most tasks.
