Abstract:
In this era of e-commerce, user’s opinion about a product on the online review system
is of great importance, as it provides guidance for people to decide. Being very important,
people used to write fake reviews about the products, called opinion spamming.
Detecting opinion spams in online review platforms is a challenging task drawing
attention from research communities. It is a persistent campaign between the spammers
and platforms. Grouped opinion spamming is the main type of opinion spamming in the
online review system these days. For that purpose, usually people make multiple accounts
to write fake reviews or they pay to crowdsourcing platforms to write fake reviews for
promotion of their product or demotion of their competitor’s product. Group spam
reviews are more damaging for online review systems as reviews from many peoples
about a product either it is positive, or negative can easily deceive peoples as compared to
a single spam review. These spam review groups should be detected so that reviews
about that product reflect genuine user opinion. Many researchers try to resolve this
problem using behavioral and linguistic features of the users and reviews. Many machine
learning models are being adapted to solve this problem but could not resolve this
problem completely. The purpose of this research work is to design a framework that
detect group spammer who targets online review systems. This framework has used
linguistic, behavioral, and structural features to dig out all such spammer groups who
write fake reviews. First, constructed a reviewer-product network between reviewers and
products. Then written an algorithm to find out collusiveness score between reviewers
who have commonly reviewed a product and constructed a network between reviewers.
Then all those edges (reviewer pairs) whose score was below a threshold valued was
removed from the network. After that extracted high quality candidate spammer groups
by using a novel algorithm. Five best group spam indicators are used to calculate
spamicity score of candidate spammer groups. The candidate groups whose spamicity
score was greater than a threshold values (says 0.6) was considered as spammer groups.
For the evaluation of proposed algorithm, experiments performed on 3 labeled datasets
from Yelp. The system extracted spammer groups from online reviews with precision of
0.89 @ top 50 spammer groups
