Illusions of truth—Experimental insights into human and algorithmic detections of fake online reviews
Plotkina, Daria Munzel, Andreas Pallud, Jessie
Plotkina, Daria
Munzel, Andreas
Pallud, Jessie
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Publication Type
Journal article with impact factor
Editor
Supervisor
Publication Year
2020
Journal
Journal of Business Research
Book
Publication Volume
109
Publication Issue
March
Publication Begin page
511
Publication End page
523
Publication NUmber of pages
Collections
Abstract
The issue of fake online reviews is increasingly relevant due to the growing importance of online reviews to consumers and the growing frequency of deceptive corporate practices. It is, therefore, necessary to be able to detect fake online reviews. An experiment with 1041 respondents allowed us to create two pools of reviews (fake and truthful) and compare them for psycholinguistic deception cues. The resulting automated tool accounted for review valence and incentive and detected deceptive reviews with 81% accuracy. A follow-up experiment with 407 consumers showed that humans have only a 57% accuracy of detection, even when a deception mindset is activated with information on cues of fake online reviews. Therefore, micro-linguistic automated detection can be used to filter the content of reviewing websites to protect online users. Our independent analysis of reviewing websites confirms the presence of dubious content and, therefore, the need to introduce more sophisticated filtering approaches.
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Keywords
Deceptive Communication, Fake Online Reviews, Human Deceit Detection, Truth Bias, Online Deception Detection, Opinion Spam