Equal rating opportunity analysis for detecting review manipulation

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. Four highly problematic products are successfully detected from 84 products.