Abstract
This research considers the problem of review manipulation by exploring rating distribution deviations and proposing a detection framework that captures review bias. We characterize the severity of review manipulation based on the degree to which the corresponding rating deviates from a normal distribution. Then we utilize deep learning approaches to learn hidden textual representations of unmanipulated reviews for biased review detection. Experiments conducted on 1,396,132 movie reviews demonstrate that highly/low biased reviews yield significant difference through our proposed model. Moreover, sentiment and topic analysis on experimental results reveal that biased reviews differentiate from authentic reviews in covering less movie-related topics and embedding stronger and more inflammatory emotions. The findings provide managerial insights for business owners in detecting review manipulation and suggest practical signals for online consumers to distinguish misleading information online.
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