Liars! Detecting Fictitious Product Reviews via a Combination of Automatic Text Analysis and Experiments
Fraudulent user-generated content is harmful for both consumers and marketers and increases uncertainty about consumption experiences and offerings. Ann Kronrod, Jeffrey Lee, and Ivan Gordeliy investigate a novel method leveraging linguistic theory, experiment-driven data sampling, and automated text analysis to test three linguistic aspects that distinguish between authentic and fictitious reviews. In this webinar, Ann Kronrod will report the results of this investigation, as well as the outcomes of human participants’ performance on a detection test. She will also explain how these findings can be used by managers of digital platforms that depend on consumer trust and on an abundance of authentic user-generated content.