  E-commerce is the fastest-growing segment of the economy. Online reviews play
a crucial role in helping consumers evaluate and compare products and services.
As a result, fake reviews (opinion spam) are becoming more prevalent and
negatively impacting customers and service providers. There are many reasons
why it is hard to identify opinion spammers automatically, including the
absence of reliable labeled data. This limitation precludes an off-the-shelf
application of a machine learning pipeline. We propose a new method for
classifying reviewers as spammers or benign, combining machine learning with a
message-passing algorithm that capitalizes on the users' graph structure to
compensate for the possible scarcity of labeled data. We devise a new way of
sampling the labels for the training step (active learning), replacing the
typical uniform sampling. Experiments on three large real-world datasets from
Yelp.com show that our method outperforms state-of-the-art active learning
approaches and also machine learning methods that use a much larger set of
labeled data for training.
