Detecting Anomalous Online Reviewers: An Unsupervised Approach Using Mixture Models

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.

Key words and phrases:

• online reviews
• fake reviews
• opinion spam
• unsupervised learning
• anomaly detection
• mixture models
• deception detection

Supplemental Material

Supplemental data for this article can be accessed on the publisher’s website.

Notes

1. See http://www.huffingtonpost.com/2013/09/25/fake-yelp-reviews_n_3983564.html, retrieved on March 19, 2019.

2. See http://www.nytimes.com/2011/08/20/technology/finding-fake-reviews-online.html, retrieved on March 19, 2019.

Additional information

Notes on contributors

Naveen Kumar

Naveen Kumar ([email protected]) is an assistant professor of Management Information Systems in the School of Business, University of Washington, Bothell. He received his Ph.D. from the University of Washington, Seattle. His research focuses on applying deep learning and other artificial intelligence techniques in social media and information systems. Before joining academia, he worked as a researcher in the high-tech industry, solving complex business problems in IT, Finance, and Manufacturing using advanced machine-learning techniques.

Subodha Kumar ([email protected]) is the Paul Anderson Distinguished Professor of Supply Chain Management, Marketing, Information Systems, and Statistical Science, and the director of the Center for Data Analytics at the Fox School of Business, Temple University. He earned his Ph.D. from the University of Texas at Dallas. Dr. Kumar has published numerous papers in a variety of journals. He is the deputy editor and a department editor of Production and Operations Management and has served as a senior editor of Decision Sciences and an associate editor of Information Systems Research.

Deepak Venugopal

Deepak Venugopal ([email protected]) is an assistant professor in the Department of Computer Science at the University of Memphis. He received his Ph.D. in computer science from the University of Texas at Dallas. His research interests focus on probabilistic and statistical relational models. Dr. Venugopal’s work has been published in the proceedings of conferences, including those of the Association for the Advancement of Artificial Intelligence, Conference on Neural Information Processing, and others.

Liangfei Qiu

Liangfei Qiu ([email protected]; corresponding author) is an associate professor in the Department of Information Systems and Operations Management at the Warrington College of Business, University of Florida. He received his Ph.D. in economics from the University of Texas at Austin. Dr. Qiu’s research focuses on economics of information systems, prediction markets, social media, and telecommunications policy. His work has been published in Decision Support Systems, Information Systems Research, Journal of Management Information Systems, MIS Quarterly, and other journals.

Subodha Kumar

Naveen Kumar ([email protected]) is an assistant professor of Management Information Systems in the School of Business, University of Washington, Bothell. He received his Ph.D. from the University of Washington, Seattle. His research focuses on applying deep learning and other artificial intelligence techniques in social media and information systems. Before joining academia, he worked as a researcher in the high-tech industry, solving complex business problems in IT, Finance, and Manufacturing using advanced machine-learning techniques.

Subodha Kumar ([email protected]) is the Paul Anderson Distinguished Professor of Supply Chain Management, Marketing, Information Systems, and Statistical Science, and the director of the Center for Data Analytics at the Fox School of Business, Temple University. He earned his Ph.D. from the University of Texas at Dallas. Dr. Kumar has published numerous papers in a variety of journals. He is the deputy editor and a department editor of Production and Operations Management and has served as a senior editor of Decision Sciences and an associate editor of Information Systems Research.