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A novel framework for automatic incentivised review detection

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Listed:
  • Syed Abdullah Ashraf
  • Aariz Faizan Javed
  • Pradip Kumar Bala
  • Rashmi Jain
  • Zainab Fatma

Abstract

Incentivised reviews are a permanent threat to the credibility of information available on a platform. They not only interfere with the consumer decision-making process but also impact the market dynamics. We have proposed a rule-based method for identifying incentivised reviews in the hospitality domain. We then extracted several features from the meta-feature. These features were transformed using mathematical functions. The study showed that power transformation along with XGBoost is best suited for the task. Our work has both practical and managerial implications. Our model is lightweight, scalable, and generalisable. Moreover, platforms can use our model with a fake review detection method to safeguard the interest of honest, hardworking sellers and buyers looking for trustworthy information. Based on our research, our work is among the first few to address incentivised review detection in the hospitality sector.

Suggested Citation

  • Syed Abdullah Ashraf & Aariz Faizan Javed & Pradip Kumar Bala & Rashmi Jain & Zainab Fatma, 2025. "A novel framework for automatic incentivised review detection," International Journal of Business Information Systems, Inderscience Enterprises Ltd, vol. 50(2), pages 215-238.
    • Handle: RePEc:ids:ijbisy:v:50:y:2025:i:2:p:215-238
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