Detecting Anomalous Online Reviewers: An Unsupervised Approach Using Mixture Models. Issue 4 (2nd October 2019)

• Record Type: Journal Article
• Authors: Kumar, Naveen; Venugopal, Deepak; Qiu, Liangfei; Kumar, Subodha
• Is Part Of: Journal of management information systems. Volume 36:Issue 4(2019)
• Page Start: 1313
• Page End: 1346
• Publication Date: 2019-10-02
• Subjects: online reviews -- fake reviews -- opinion spam -- unsupervised learning -- anomaly detection -- mixture models -- deception detection; Management information systems -- Periodicals; Management information systems; Periodicals; 658.4038011
• DOI: 10.1080/07421222.2019.1661089
• Languages: English
• ISSNs: 0742-1222

Abstract

Abstract: Online reviews play a significant role in influencing decisions made by users in day-to-day life. The presence of reviewers who deliberately post fake reviews for financial or other gains, however, negatively impacts both users and businesses. Unfortunately, automatically detecting such reviewers is a challenging problem since fake reviews do not seem out-of-place next to genuine reviews. In this paper, we present a fully unsupervised approach to detect anomalous behavior in online reviewers. We propose a novel hierarchical approach for this task in which we (1) derive distributions for key features that define reviewer behavior, and (2) combine these distributions into a finite mixture model. Our approach is highly generalizable and it allows us to seamlessly combine both univariate and multivariate distributions into a unified anomaly detection system. Most importantly, it requires no explicit labeling (spam/not spam) of the data. Our newly developed approach outperforms prior state-of-the-art unsupervised anomaly detection approaches.