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Abstract—Online review plays an important role when people are making decisions to purchase a product or service. It is shown that sellers can benefit from boosting their product review or downgrading their competitors’ product review. Dishonest behavior on reviews can seriously affect both buyers and sellers. In this paper, we introduce a novel angle to detect dishonest reviews, called Equal Rating Opportunity (ERO) evaluation. The proposed ERO evaluation can detect embedded manipulation signals based on limited amount of data. Experiments based on real data are conducted.

Abstract—Online review plays an important role when people are making decisions to purchase a product or service. It is shown that sellers can benefit from boosting their product review or downgrading their competitors’ product review. Dishonest behavior on reviews can seriously affect both buyers and sellers. In this paper, we introduce a novel angle to detect dishonest reviews, called Equal Rating Opportunity (ERO) evaluation. The proposed ERO evaluation can detect embedded manipulation signals based on limited amount of data. Experiments based on real data are conducted.

Online review plays an important role when people
are making decisions to purchase a product/service. It is shown
that the sellers can benefit from boosting the reviews of their
products/services, or downgrading the reviews of their competitors.
Dishonest behavior on reviews can seriously affect both buyers
and sellers. In this work, we propose an algorithm that contains
a two-step analysis to detect whether a product’s reviews have
been manipulated. The first step is called consistency analysis,