  Unsafe websites consist of malicious as well as inappropriate sites, such as
those hosting questionable or offensive content. Website reputation systems are
intended to help ordinary users steer away from these unsafe sites. However,
the process of assigning safety ratings for websites typically involves humans.
Consequently it is time consuming, costly and not scalable. This has resulted
in two major problems: (i) a significant proportion of the web space remains
unrated and (ii) there is an unacceptable time lag before new websites are
rated.
  In this paper, we show that by leveraging structural and content-based
properties of websites, it is possible to reliably and efficiently predict
their safety ratings, thereby mitigating both problems. We demonstrate the
effectiveness of our approach using four datasets of up to 90,000 websites. We
use ratings from Web of Trust (WOT), a popular crowdsourced web reputation
system, as ground truth. We propose a novel ensemble classification technique
that makes opportunistic use of available structural and content properties of
webpages to predict their eventual ratings in two dimensions used by WOT:
trustworthiness and child safety. Ours is the first classification system to
predict such subjective ratings and the same approach works equally well in
identifying malicious websites. Across all datasets, our classification
performs well with average F$_1$-score in the 74--90\% range.
