  Over the last years, online reviews became very important since they can
influence the purchase decision of consumers and the reputation of businesses,
therefore, the practice of writing fake reviews can have severe consequences on
customers and service providers. Various approaches have been proposed for
detecting opinion spam in online reviews, especially based on supervised
classifiers. In this contribution, we start from a set of effective features
used for classifying opinion spam and we re-engineered them, by considering the
Cumulative Relative Frequency Distribution of each feature. By an experimental
evaluation carried out on real data from Yelp.com, we show that the use of the
distributional features is able to improve the performances of classifiers.
