Title: A novel framework for automatic incentivised review detection

Authors: Syed Abdullah Ashraf; Aariz Faizan Javed; Pradip Kumar Bala; Rashmi Jain; Zainab Fatma

Addresses: Department of Information Systems & Analytics, Jindal Global Business School, O. P. Jindal Global University, Sonipat, Haryana, India ' Indian Institute of Management Ranchi, 5th Floor, Suchana Bhawan, Meur's Road, Audrey House Campus, Ranchi, Jharkhand, 834008, India ' Indian Institute of Management Ranchi, 5th Floor, Suchana Bhawan, Meur's Road, Audrey House Campus, Ranchi, Jharkhand, 834008, India ' Department of Information Management and Business Analytics, Feliciano School of Business, Montclair State University, 1 Normal Ave, Montclair, NJ, 07043, USA ' Department of English, Aligarh Muslim University, Aligarh-202002, India

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.

Keywords: user-generated content; incentivised reviews detection; incentivised reviews identification; natural language processing; text processing; Yelp; Amazon; Tripadvisor; trust; credibility; fake reviews; feature transformation; feature engineering.

DOI: 10.1504/IJBIS.2025.148794

International Journal of Business Information Systems, 2025 Vol.50 No.2, pp.215 - 238

Received: 14 Oct 2021
Accepted: 24 Dec 2021
Published online: 25 Sep 2025 *

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