Finding Deceptive Opinion Spam by Any Stretch of the Imagination

TL;DR

This paper develops and compares methods for detecting deceptive opinion spam, achieving nearly 90% accuracy, and reveals a link between deceptive opinions and imaginative writing.

Contribution

It introduces three approaches to identify deceptive opinion spam and provides a theoretical analysis connecting deception to imaginative language use.

Findings

Classifier achieves nearly 90% accuracy

Identifies linguistic features linked to deception

Reveals relationship between deception and imagination

Abstract

Consumers increasingly rate, review and research products online. Consequently, websites containing consumer reviews are becoming targets of opinion spam. While recent work has focused primarily on manually identifiable instances of opinion spam, in this work we study deceptive opinion spam---fictitious opinions that have been deliberately written to sound authentic. Integrating work from psychology and computational linguistics, we develop and compare three approaches to detecting deceptive opinion spam, and ultimately develop a classifier that is nearly 90% accurate on our gold-standard opinion spam dataset. Based on feature analysis of our learned models, we additionally make several theoretical contributions, including revealing a relationship between deceptive opinions and imaginative writing.