  Online reviews play a crucial role in helping consumers evaluate and compare
products and services. However, review hosting sites are often targeted by
opinion spamming. In recent years, many such sites have put a great deal of
effort in building effective review filtering systems to detect fake reviews
and to block malicious accounts. Thus, fraudsters or spammers now turn to
compromise, purchase or even raise reputable accounts to write fake reviews.
Based on the analysis of a real-life dataset from a review hosting site
(dianping.com), we discovered that reviewers' posting rates are bimodal and the
transitions between different states can be utilized to differentiate spammers
from genuine reviewers. Inspired by these findings, we propose a two-mode
Labeled Hidden Markov Model to detect spammers. Experimental results show that
our model significantly outperforms supervised learning using linguistic and
behavioral features in identifying spammers. Furthermore, we found that when a
product has a burst of reviews, many spammers are likely to be actively
involved in writing reviews to the product as well as to many other products.
We then propose a novel co-bursting network for detecting spammer groups. The
co-bursting network enables us to produce more accurate spammer groups than the
current state-of-the-art reviewer-product (co-reviewing) network.
