Online shoppers could one day face fewer misleading fake reviews thanks to a newly tested AI-powered detection system developed by researchers at the University of East London.
Fake reviews are a growing problem for consumers and online businesses, especially with the growth in AI generated content. According to the researchers, from the Royal Docks School of Business and Law , misleading reviews can distort competition, damage trust in online marketplaces and persuade people to buy poor-quality or even unsafe products.
The new system combines AI language analysis with behavioural clues such as whether the emotional tone of a review matches its star rating, how long the review is and other patterns linked to suspicious activity. The researchers, from the Royal Docks School of Business and Law, say this gives the model a fuller picture of whether a review is genuine or deceptive.
The new study , published in FinTech and Sustainable Innovation , describes a new “hybrid fusion” model designed to identify fraudulent reviews on platforms such as Amazon and Yelp.
Unlike older systems that mainly relied on keywords or simple patterns, the new approach is designed to understand the meaning and context behind written reviews. That helps it detect more convincing fake reviews that might otherwise appear genuine to shoppers.
In testing, the model achieved 93% accuracy on Amazon review data and 91% accuracy on Yelp reviews, outperforming several traditional detection methods examined in the study.
Co-author Dr Hisham AbouGrad said, “Fake reviews are becoming increasingly sophisticated and harder to detect. Our findings show that combining AI language understanding with behavioural signals can provide a more reliable way to identify misleading reviews and help strengthen trust in online marketplaces.”
Co-author Fiza Riaz said, “This research shows that AI systems can move beyond simply spotting suspicious words. By looking at context and behaviour together, the model can better recognise patterns linked to deceptive reviews while still supporting genuine customer feedback.”
The paper says the next stage of the research will focus on improving the system using larger and more varied datasets, exploring newer AI models and studying how the technology could eventually work in real-time on large e-commerce platforms.
AbouGrad, H, & Riaz, F (2026). Metadata-Enhanced Hybrid Fusion Architecture: Commercial Fake Reviews Detection Model Using Transformer Embeddings . FinTech and Sustainable Innovation . https://doi.org/10.47852/bonviewFSI62028859
Experimental study
Metadata-Enhanced Hybrid Fusion Architecture: Commercial Fake Reviews Detection Model Using Transformer Embeddings
13-May-2026