Catch Me If You Can — Detecting Fraudulent Online Reviews of Doctors Using Deep Learning

38 Pages Posted: 24 Feb 2019

See all articles by Aishwarya Shukla

Aishwarya Shukla

Simon Fraser University, Beedie School of Business

Weiguang Wang

University of Maryland, Robert H. Smith School of Business

Guodong (Gordon) Gao

University of Maryland - R.H. Smith School of Business

Ritu Agarwal

University of Maryland - Robert H. Smith School of Business

Date Written: January 14, 2019

Abstract

Fake online reviews are becoming more prevalent and are a significant concern for consumer protection groups and regulatory authorities. However, identifying fake reviews has been a challenge in IS, marketing, and computer science. In this study, we design a deep learning approach to capture the linguistic traits that differentiate between genuine and fake reviews. Our deep learning model is evaluated on a dataset of 181,951 doctor reviews, 8% of which are fake. Since a natural honeypot existed at one point on the platform that hosted these reviews, we are able to label the reviews that exploited the natural honeypot as fraudulent, thus overcoming the major challenge in constructing the ground truth for training the model. Our model shows a significant improvement in accuracy when compared to traditional machine learning algorithms such as logistic regression and random forest. Interestingly, we also find that human evaluators perform much worse than machine learning approaches. Compared to 200 human evaluators, our deep learning approach has a true positive rate (14.29% vs. 8.70%) that is twice as high, and it also achieves a much lower false positive rate (0.63% vs. 11.68%). We also observe that these evaluators are susceptible to human bias, as they are more likely to label fake reviews as genuine than they are to label genuine reviews as genuine. Our study offers further explanations for the advantages of deep learning and is the first to construct a deep learning model to detect fraudulent online reviews, an approach that can help curb fake reviews and increase information quality and market efficiency.

Keywords: Deep Learning, Online Reviews, Fraud Detection, Human Biases, Healthcare

JEL Classification: M01

Suggested Citation

Shukla, Aishwarya and Wang, Weiguang and Gao, Guodong (Gordon) and Agarwal, Ritu, Catch Me If You Can — Detecting Fraudulent Online Reviews of Doctors Using Deep Learning (January 14, 2019). Available at SSRN: https://ssrn.com/abstract=3320258 or http://dx.doi.org/10.2139/ssrn.3320258

Aishwarya Shukla (Contact Author)

Simon Fraser University, Beedie School of Business ( email )

Vancouver, British Columbia V5A1S6
Canada

Weiguang Wang

University of Maryland, Robert H. Smith School of Business ( email )

College Park, MD
United States

Guodong (Gordon) Gao

University of Maryland - R.H. Smith School of Business ( email )

4325 Van Munching Hall
College Park, MD 20742
United States

HOME PAGE: http://www.rhsmith.umd.edu/faculty/ggao/

Ritu Agarwal

University of Maryland - Robert H. Smith School of Business ( email )

College Park, MD 20742-1815
United States

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