  Online marketplaces often witness opinion spam in the form of reviews. People
are often hired to target specific brands for promoting or impeding them by
writing highly positive or negative reviews. This often is done collectively in
groups. Although some previous studies attempted to identify and analyze such
opinion spam groups, little has been explored to spot those groups who target a
brand as a whole, instead of just products.
  In this paper, we collected reviews from the Amazon product review site and
manually labelled a set of 923 candidate reviewer groups. The groups are
extracted using frequent itemset mining over brand similarities such that users
are clustered together if they have mutually reviewed (products of) a lot of
brands. We hypothesize that the nature of the reviewer groups is dependent on 8
features specific to a (group, brand) pair. We develop a feature-based
supervised model to classify candidate groups as extremist entities. We run
multiple classifiers for the task of classifying a group based on the reviews
written by the users of that group, to determine if the group shows signs of
extremity. A 3-layer Perceptron based classifier turns out to be the best
classifier. We further study the behaviours of such groups in detail to
understand the dynamics of brand-level opinion fraud better. These behaviours
include consistency in ratings, review sentiment, verified purchase, review
dates and helpful votes received on reviews. Surprisingly, we observe that
there are a lot of verified reviewers showing extreme sentiment, which on
further investigation leads to ways to circumvent existing mechanisms in place
to prevent unofficial incentives on Amazon.
