Ana Marta SantosAntonio, Nuno2026-03-262026-03-262025-06-241098-3058http://hdl.handle.net/10400.1/28550In the hotel industry, social reputation is critical. Consumers increasingly rely on online reviews for accommodation decisions, making Artificial Intelligence (AI) generated fraudulent reviews a significant threat. Distinguishing between genuine and AI-generated reviews is essential for hotels to maintain credibility. This study creates a unique dataset of AI-generated reviews and combines vectorization methods with text-based features to build a Machine Learning model for identifying nongenuine reviews. Results show that incorporating text-based features significantly improves detection accuracy, and simpler vectorization methods can be effective for simpler datasets. This study contributes to academia by providing a novel methodology and publicly available dataset for further research, and to the hotel industry by enhancing credibility and consumer trust through better review filtering.engFraudulent reviewsAI-generatedNatural language processingMachine learningVectorization methodsjournal article10.1007/s40558-025-00329-z1943-4294