Illusions of truth—Experimental insights into human and algorithmic detections of fake online reviews
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1 mars 2020
- handle: 10670/1.gmqq9k
- hal: hal-02423585
- doi: 10.1016/j.jbusres.2018.12.009
- PII: S0148-2963(18)30619-2
Ce document est lié à :
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.jbusres.2018.12.009
http://creativecommons.org/licenses/by-nc/ , info:eu-repo/semantics/OpenAccess
Mots-clés
Deceptive communication Fake online reviews Human deceit detection Truth bias Online deception detection Opinion spamSujets proches
Reviews Reviews Reviews Theater reviews Reviews Television plays--Reviews Television reviews Reviews Newspapers--Sections, columns, etc.--Book section Book reviews ReviewsCiter ce document
Daria Plotkina et al., « Illusions of truth—Experimental insights into human and algorithmic detections of fake online reviews », HAL-SHS : droit et gestion, ID : 10.1016/j.jbusres.2018.12.009
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Résumé
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.