Machine Learning Based Framework for Unmasking Bogus Reviews in Online Shopping

Abstract

This research introduces a robust machine learning framework that utilizes the K-Nearest Neighbors (KNN) algorithm to detect fake reviews in Amazon product feedback. The model capitalizes on KNN's ability to assess the proximity of data points, integrating a diverse range of features derived from the textual content, temporal patterns, and contextual elements of reviews. By thoroughly analyzing these features, the model is able to identify subtle discrepancies that distinguish genuine feedback from deceptive ones. Rigorous validation on real-world datasets demonstrates the model's high accuracy in detecting fake reviews, while also maintaining a balance between effectiveness and computational efficiency. The model's design ensures it is adaptable across various product categories and scales well within Amazon's vast ecosystem, addressing the complexities of diverse product offerings. Furthermore, the approach is engineered to be resilient against evolving deceptive tactics and variations across different regions and time periods, showcasing its robustness and long-term applicability. The study highlights the importance of adopting KNN-based methodologies as a critical tool in the ongoing battle to preserve the integrity of online feedback systems. By enhancing the reliability of reviews, this framework empowers consumers with trustworthy information, enabling them to make informed purchasing decisions. The findings of this research advocate for the broader implementation of KNN-driven approaches to fortify consumer trust and ensure the credibility of e-commerce platforms.