Detection of opinion spam based on anomalous rating deviation
release_2nlws632qzc2bnrgxuy5mysrpa
by
David Savage, Xiuzhen Zhang, Xinghuo Yu, Pauline Chou, Qingmai Wang
2016
Abstract
The publication of fake reviews by parties with vested interests has become a
severe problem for consumers who use online product reviews in their decision
making. To counter this problem a number of methods for detecting these fake
reviews, termed opinion spam, have been proposed. However, to date, many of
these methods focus on analysis of review text, making them unsuitable for many
review systems where accom-panying text is optional, or not possible. Moreover,
these approaches are often computationally expensive, requiring extensive
resources to handle text analysis over the scale of data typically involved.
In this paper, we consider opinion spammers manipulation of average ratings
for products, focusing on dif-ferences between spammer ratings and the majority
opinion of honest reviewers. We propose a lightweight, effective method for
detecting opinion spammers based on these differences. This method uses
binomial regression to identify reviewers having an anomalous proportion of
ratings that deviate from the majority opinion. Experiments on real-world and
synthetic data show that our approach is able to successfully iden-tify opinion
spammers. Comparison with the current state-of-the-art approach, also based
only on ratings, shows that our method is able to achieve similar detection
accuracy while removing the need for assump-tions regarding probabilities of
spam and non-spam reviews and reducing the heavy computation required for
learning.
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