IDEAS home Printed from https://ideas.repec.org/p/hal/journl/hal-02423585.html
   My bibliography  Save this paper

Illusions of truth—Experimental insights into human and algorithmic detections of fake online reviews

Author

Listed:
  • Daria Plotkina

    (Humanis - Hommes et management en société / Humans and management in society - UNISTRA - Université de Strasbourg)

  • Andreas Munzel

    (TSM - Toulouse School of Management Research - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - CNRS - Centre National de la Recherche Scientifique - TSM - Toulouse School of Management - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse)

  • Jessie Pallud

    (Humanis - Hommes et management en société / Humans and management in society - UNISTRA - Université de Strasbourg)

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.

Suggested Citation

  • Daria Plotkina & Andreas Munzel & Jessie Pallud, 2020. "Illusions of truth—Experimental insights into human and algorithmic detections of fake online reviews," Post-Print hal-02423585, HAL.
    • Handle: RePEc:hal:journl:hal-02423585
    DOI: 10.1016/j.jbusres.2018.12.009
    Note: View the original document on HAL open archive server: https://hal.umontpellier.fr/hal-02423585
    as

    Download full text from publisher

    File URL: https://hal.umontpellier.fr/hal-02423585/document
    Download Restriction: no
    File URL: https://libkey.io/10.1016/j.jbusres.2018.12.009?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Chatterjee, Sheshadri & Chaudhuri, Ranjan & Kumar, Ajay & Lu Wang, Cheng & Gupta, Shivam, 2023. "Impacts of consumer cognitive process to ascertain online fake review: A cognitive dissonance theory approach," Journal of Business Research, Elsevier, vol. 154(C).
    2. Josef Zelenka & Tracy Azubuike & Martina Pásková, 2021. "Trust Model for Online Reviews of Tourism Services and Evaluation of Destinations," Administrative Sciences, MDPI, vol. 11(2), pages 1-21, March.
    3. Tim Kollmer & Andreas Eckhardt & Victoria Reibenspiess, 2022. "Explaining consumer suspicion: insights of a vignette study on online product reviews," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(3), pages 1221-1238, September.
    4. Mark Ryan & Josephina Antoniou & Laurence Brooks & Tilimbe Jiya & Kevin Macnish & Bernd Stahl, 2020. "The Ethical Balance of Using Smart Information Systems for Promoting the United Nations’ Sustainable Development Goals," Sustainability, MDPI, vol. 12(12), pages 1-22, June.
    5. Salminen, Joni & Kandpal, Chandrashekhar & Kamel, Ahmed Mohamed & Jung, Soon-gyo & Jansen, Bernard J., 2022. "Creating and detecting fake reviews of online products," Journal of Retailing and Consumer Services, Elsevier, vol. 64(C).
    6. Wu, Ruhai & Qiu, Chun, 2023. "When Karma strikes back: A model of seller manipulation of consumer reviews in an online marketplace," Journal of Business Research, Elsevier, vol. 155(PB).
    7. Hajek, Petr & Sahut, Jean-Michel, 2022. "Mining behavioural and sentiment-dependent linguistic patterns from restaurant reviews for fake review detection," Technological Forecasting and Social Change, Elsevier, vol. 177(C).
    8. Banerjee, Snehasish & Chua, Alton Y.K., 2023. "Understanding online fake review production strategies," Journal of Business Research, Elsevier, vol. 156(C).
    9. Zaman, Mustafeed & Vo-Thanh, Tan & Nguyen, Chi T.K. & Hasan, Rajibul & Akter, Shahriar & Mariani, Marcello & Hikkerova, Lubica, 2023. "Motives for posting fake reviews: Evidence from a cross-cultural comparison," Journal of Business Research, Elsevier, vol. 154(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hal:journl:hal-02423585. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.