IDEAS home Printed from https://ideas.repec.org/a/eee/tefoso/v198y2024ics004016252300656x.html
   My bibliography  Save this article

Investigating reviewers' intentions to post fake vs. authentic reviews based on behavioral linguistic features

Author

Listed:
  • Kim, Jong Min
  • Park, Keeyeon Ki-cheon
  • Mariani, Marcello
  • Wamba, Samuel Fosso

Abstract

Growing interest in peer-generated online reviews for product promotion has incentivized online review manipulation. The latter is challenging to be detected. In this study, to discern reviews that are likely authentic vs. fake, we leverage interpersonal deception theory (IDT) and then investigate verbal and nonverbal features that reflect reviewers' intentions to post fake vs. authentic reviews by using topic modeling techniques. Our findings show topic differences between fake vs. authentic reviews. Based on the results, review manipulators tend to write reviews recommending particular movies, while authentic reviewers are likely to provide movie content information in their reviews. Also, we reveal that review manipulation happens at the early stage of product diffusion and contributes to increasing review ratings. Lastly, we discover that manipulated/fake reviews are more informative and positive. Our findings contribute to extend research on online fake reviews literature by innovatively examining review-writing intentions with topic differences, sentiment, and informativeness. To the best of our knowledge, this is the first attempt to introduce topic factors in the fake review detection literature.

Suggested Citation

  • Kim, Jong Min & Park, Keeyeon Ki-cheon & Mariani, Marcello & Wamba, Samuel Fosso, 2024. "Investigating reviewers' intentions to post fake vs. authentic reviews based on behavioral linguistic features," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
  • Handle: RePEc:eee:tefoso:v:198:y:2024:i:c:s004016252300656x
    DOI: 10.1016/j.techfore.2023.122971
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S004016252300656X
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.techfore.2023.122971?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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).
    2. Dina Mayzlin & Yaniv Dover & Judith Chevalier, 2014. "Promotional Reviews: An Empirical Investigation of Online Review Manipulation," American Economic Review, American Economic Association, vol. 104(8), pages 2421-2455, August.
    3. Mariani, Marcello M. & Fosso Wamba, Samuel, 2020. "Exploring how consumer goods companies innovate in the digital age: The role of big data analytics companies," Journal of Business Research, Elsevier, vol. 121(C), pages 338-352.
    4. Aishwarya Deep Shukla & Guodong (Gordon) Gao & Ritu Agarwal, 2021. "How Digital Word-of-Mouth Affects Consumer Decision Making: Evidence from Doctor Appointment Booking," Management Science, INFORMS, vol. 67(3), pages 1546-1568, March.
    5. 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).
    6. Kumar, Aman & Shankar, Amit & Behl, Abhishek & Arya, Varsha & Gupta, Nakul, 2023. "Should I share it? Factors influencing fake news-sharing behaviour: A behavioural reasoning theory perspective," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    7. Cano-Marin, Enrique & Mora-Cantallops, Marçal & Sanchez-Alonso, Salvador, 2023. "The power of big data analytics over fake news: A scientometric review of Twitter as a predictive system in healthcare," Technological Forecasting and Social Change, Elsevier, vol. 190(C).
    8. Kim, Jong Min & Lee, Eunkyung & Mariani, Marcello M., 2021. "The influence of launching mobile channels on online customer reviews," Journal of Business Research, Elsevier, vol. 137(C), pages 366-378.
    9. Hajek, Petr & Hikkerova, Lubica & Sahut, Jean-Michel, 2023. "Fake review detection in e-Commerce platforms using aspect-based sentiment analysis," Journal of Business Research, Elsevier, vol. 167(C).
    10. Kim, Jong Min & Park, Keeyeon Ki-cheon & Mariani, Marcello M., 2023. "Do online review readers react differently when exposed to credible versus fake online reviews?," Journal of Business Research, Elsevier, vol. 154(C).
    11. Hunt Allcott & Matthew Gentzkow, 2017. "Social Media and Fake News in the 2016 Election," NBER Working Papers 23089, National Bureau of Economic Research, Inc.
    12. Lina Zhou & Yu-wei Sung & Dongsong Zhang, 2013. "Deception Performance in Online Group Negotiation and Decision Making: The Effects of Deception Experience and Deception Skill," Group Decision and Negotiation, Springer, vol. 22(1), pages 153-172, January.
    13. Uttara M. Ananthakrishnan & Beibei Li & Michael D. Smith, 2020. "A Tangled Web: Should Online Review Portals Display Fraudulent Reviews?," Information Systems Research, INFORMS, vol. 31(3), pages 950-971, September.
    14. Akter, Shahriar & Motamarri, Saradhi & Hani, Umme & Shams, Riad & Fernando, Mario & Mohiuddin Babu, Mujahid & Ning Shen, Kathy, 2020. "Building dynamic service analytics capabilities for the digital marketplace," Journal of Business Research, Elsevier, vol. 118(C), pages 177-188.
    15. Hunt Allcott & Matthew Gentzkow, 2017. "Social Media and Fake News in the 2016 Election," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 211-236, Spring.
    16. Kahn, Kim Fridkin & Kenney, Patrick J., 2002. "The Slant of the News: How Editorial Endorsements Influence Campaign Coverage and Citizens' Views of Candidates," American Political Science Review, Cambridge University Press, vol. 96(2), pages 381-394, June.
    17. Moon, Sangkil & Kim, Moon-Yong & Iacobucci, Dawn, 2021. "Content analysis of fake consumer reviews by survey-based text categorization," International Journal of Research in Marketing, Elsevier, vol. 38(2), pages 343-364.
    18. Mariani, Marcello M. & Machado, Isa & Magrelli, Vittoria & Dwivedi, Yogesh K., 2023. "Artificial intelligence in innovation research: A systematic review, conceptual framework, and future research directions," Technovation, Elsevier, vol. 122(C).
    19. Casaló, Luis V. & Flavián, Carlos & Guinalíu, Miguel & Ekinci, Yuksel, 2015. "Avoiding the dark side of positive online consumer reviews: Enhancing reviews' usefulness for high risk-averse travelers," Journal of Business Research, Elsevier, vol. 68(9), pages 1829-1835.
    20. Michael Luca & Georgios Zervas, 2016. "Fake It Till You Make It: Reputation, Competition, and Yelp Review Fraud," Management Science, INFORMS, vol. 62(12), pages 3412-3427, December.
    21. Ozbay, Feyza Altunbey & Alatas, Bilal, 2020. "Fake news detection within online social media using supervised artificial intelligence algorithms," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chatterjee, Sheshadri & Mariani, Marcello & Fosso Wamba, Samuel, 2023. "Prosumers’ intention to co-create business value and the moderating role of digital media usage," Journal of Business Research, Elsevier, vol. 163(C).
    2. Harrison-Walker, L. Jean & Jiang, Ying, 2023. "Suspicion of online product reviews as fake: Cues and consequences," Journal of Business Research, Elsevier, vol. 160(C).
    3. Hajek, Petr & Hikkerova, Lubica & Sahut, Jean-Michel, 2023. "Fake review detection in e-Commerce platforms using aspect-based sentiment analysis," Journal of Business Research, Elsevier, vol. 167(C).
    4. Wen Zhang & Qiang Wang & Jian Li & Zhenzhong Ma & Gokul Bhandari & Rui Peng, 2023. "What makes deceptive online reviews? A linguistic analysis perspective," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-14, December.
    5. Mariani, Marcello M. & Borghi, Matteo & Laker, Benjamin, 2023. "Do submission devices influence online review ratings differently across different types of platforms? A big data analysis," Technological Forecasting and Social Change, Elsevier, vol. 189(C).
    6. Costa Filho, Murilo & Nogueira Rafael, Diego & Salmonson Guimarães Barros, Lucia & Mesquita, Eduardo, 2023. "Mind the fake reviews! Protecting consumers from deception through persuasion knowledge acquisition," Journal of Business Research, Elsevier, vol. 156(C).
    7. Jiexun Li & Xiaohui Chang, 2023. "Combating Misinformation by Sharing the Truth: a Study on the Spread of Fact-Checks on Social Media," Information Systems Frontiers, Springer, vol. 25(4), pages 1479-1493, August.
    8. Grunewald, Andreas & Kräkel, Matthias, 2022. "Information manipulation and competition," Games and Economic Behavior, Elsevier, vol. 131(C), pages 245-263.
    9. Mardumyan, Anna & Siret, Iris, 2023. "When review verification does more harm than good: How certified reviews determine customer–brand relationship quality," Journal of Business Research, Elsevier, vol. 160(C).
    10. Garz, Marcel & Szucs, Ferenc, 2023. "Algorithmic selection and supply of political news on Facebook," Information Economics and Policy, Elsevier, vol. 62(C).
    11. 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).
    12. Birim, Şule Öztürk & Kazancoglu, Ipek & Kumar Mangla, Sachin & Kahraman, Aysun & Kumar, Satish & Kazancoglu, Yigit, 2022. "Detecting fake reviews through topic modelling," Journal of Business Research, Elsevier, vol. 149(C), pages 884-900.
    13. Sherry He & Brett Hollenbeck & Davide Proserpio, 2022. "The Market for Fake Reviews," Marketing Science, INFORMS, vol. 41(5), pages 896-921, September.
    14. Ben Jabeur, Sami & Ballouk, Hossein & Ben Arfi, Wissal & Sahut, Jean-Michel, 2023. "Artificial intelligence applications in fake review detection: Bibliometric analysis and future avenues for research," Journal of Business Research, Elsevier, vol. 158(C).
    15. Balasubramanian Palani & Sivasankar Elango, 2023. "CTrL-FND: content-based transfer learning approach for fake news detection on social media," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(3), pages 903-918, June.
    16. Li, Yuanshuo & Zhang, Zili & Pedersen, Susanne & Liu, Xudong & Zhang, Ziqiong, 2023. "The influence of relative popularity on negative fake reviews: A case study on restaurant reviews," Journal of Business Research, Elsevier, vol. 162(C).
    17. Shawn Berry, 2024. "Fake Google restaurant reviews and the implications for consumers and restaurants," Papers 2401.11345, arXiv.org, revised Apr 2024.
    18. Wang, Qiang & Zhang, Wen & Li, Jian & Ma, Zhenzhong, 2023. "Complements or confounders? A study of effects of target and non-target features on online fraudulent reviewer detection," Journal of Business Research, Elsevier, vol. 167(C).
    19. Qiang Wang & Wen Zhang & Jian Li & Feng Mai & Zhenzhong Ma, 2024. "The devil is in the details! Effect of differentiated platform governance on online review manipulation," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-14, December.
    20. Ko, Eunhee Emily & Bowman, Douglas, 2023. "Suspicious online product reviews: An empirical analysis of brand and product characteristics using Amazon data," International Journal of Research in Marketing, Elsevier, vol. 40(4), pages 898-911.

    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:eee:tefoso:v:198:y:2024:i:c:s004016252300656x. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Catherine Liu (email available below). General contact details of provider: http://www.sciencedirect.com/science/journal/00401625 .

    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.