AI-Driven Detection and Verification System for Identifying Counterfeit Commercial Products

This research addresses the issue of “opinion spam,” where fraudulent reviews are intentionally submitted to manipulate consumer opinions and influence purchasing decisions. We propose a novel approach utilizing the AdaBoost Ensemble Decision Tree algorithm to effectively detect and remove fraudulent reviews from online platforms. The experimental results demonstrate a high accuracy rate of 95%, surpassing the performance of existing methods. By effectively identifying and mitigating the impact of opinion spam, this approach can enhance the reliability and trustworthiness of online reviews, empowering consumers to make informed purchasing decisions.