Stranger Danger on Online Digital Platforms: An Empirical Evaluation of Detecting Review Manipulation

Posted: 23 Nov 2016

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: November 21, 2016

Abstract

Opinion spammers exploit consumer trust by posting false or deceptive reviews that may have a negative impact on both consumers and businesses. These dishonest posts are difficult to detect because of complex interactions between several user characteristics such as review velocity, volume, and variety. We propose a novel hierarchical supervised learning approach to increase the likelihood of detecting anomalies by analyzing several user features and then characterizing their collective behavior in a unified manner. Specifically, we model user characteristics and interactions among them as univariate and multivariate distributions. We then stack these distributions using several supervised learning techniques, such as Logistic Regression, Support Vector Machines, and K-Nearest Neighbors yielding robust meta-classifiers. We perform a detailed evaluation of methods and then develop insights. This approach is of interest to online business platforms because it can help eliminate false reviews and increase consumer confidence in the credibility of their online information.

Keywords: digital platforms, review manipulation, information credibility, hierarchical supervised learning

Suggested Citation

Kumar, Naveen and Venugopal, Deepak and Qiu, Liangfei and Kumar, Subodha, Stranger Danger on Online Digital Platforms: An Empirical Evaluation of Detecting Review Manipulation (November 21, 2016). Mays Business School Research Paper No. 2874072. Available at SSRN: https://ssrn.com/abstract=2874072

Naveen Kumar (Contact Author)

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
Memphis, TN usa 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

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

Philadelphia, PA 19122
United States

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