Detecting Anomalous Online Reviewers: An Unsupervised Approach Using Mixture Models

Posted: 24 Oct 2018

See all articles by Naveen Kumar

Naveen Kumar

School of Business, University of Washington Bothell; University of Memphis

Deepak Venugopal

University of Memphis

Liangfei Qiu

University of Florida - Warrington College of Business Administration

Subodha Kumar

Temple University - Department of Marketing and Supply Chain Management

Date Written: October 1, 2018

Abstract

Online reviews and discussions play a significant role in influencing decisions made by users in day-to-day life. However, the presence of reviewers who deliberately post fake or deceptive reviews for financial or other gains negatively impacts both users and businesses. Unfortunately, automatically detecting such reviewers is well known to be a challenging problem, particularly since fake reviews do not seem out-of-context as compared 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, allows us to seamlessly combine both univariate and multivariate distributions into a unified anomaly detection system and most importantly requires no explicit labeling (spam/not spam) of the data. We evaluate our approach on real-world customer reviews for restaurants taken from Yelp.com. Our newly developed approach using Gaussian mixture models and one-class support vector machines outperforms prior unsupervised anomaly detection approaches. Furthermore, we also show that our approach outperforms recently developed state-of-the-art unsupervised methods based on probabilistic graphical models for identifying fake reviewers on Yelp.

Keywords: Opinion Spam, Unsupervised Learning, Anomaly Detection, Mixture Models

Suggested Citation

Kumar, Naveen and Venugopal, Deepak and Qiu, Liangfei and Kumar, Subodha, Detecting Anomalous Online Reviewers: An Unsupervised Approach Using Mixture Models (October 1, 2018). Available at SSRN: https://ssrn.com/abstract=3258708

Naveen Kumar

School of Business, University of Washington Bothell ( email )

18115 Campus Way NE
Bothell, WA 98011
United States

University of Memphis ( email )

Memphis, TN 38152-3370
United States

Deepak Venugopal

University of Memphis ( email )

Memphis, TN 38152-3370
United States

Liangfei Qiu

University of Florida - Warrington College of Business Administration ( email )

Gainesville, FL 32611
United States

HOME PAGE: http://sites.google.com/site/qiuliangfei/

Subodha Kumar (Contact Author)

Temple University - Department of Marketing and Supply Chain Management ( email )

Philadelphia, PA 19122
United States

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