Fake Reviews Detection using Machine Learning

Fake Reviews Detection using Machine Learning

Author Name : Mr. Vikram R, Srinath C, Mohammed Shuaib A, Rabin Benedict J

ABSTRACT Fake reviews pose a significant challenge to online platforms, impacting consumer trust and business credibility. The detection of deceptive reviews has gained considerable attention due to their potential to mislead customers and manipulate product or service reputations. Most existing fake review detection approaches primarily rely on semantic analysis of review content. However, these methods often fail to capture deeper patterns in user behavior and review characteristics. In this project, we propose an advanced fake review detection system leveraging the XGBoost algorithm, a powerful machine learning model known for its efficiency and accuracy in classification tasks. By analyzing multiple features such as textual content, user behavior, and metadata patterns, our approach enhances the identification of fraudulent reviews with greater precision. This study also provides a comprehensive review of the existing literature on Fake Review Detection (FRD), covering fundamental research advancements and commercial implementations. Despite the ongoing efforts, current approaches and regulations have shown limited success in effectively mitigating deceptive reviews due to evolving fraudulent tactics and the sophistication of automated review generation techniques. Our proposed system aims to address these challenges by integrating machine learning-based classification with robust feature engineering. By improving the accuracy and reliability of fake review detection, this research contributes to the development of more transparent and trustworthy online platforms. The findings of this study can be leveraged by e-commerce websites, review aggregators, and regulatory bodies to enhance fraud prevention mechanisms and safeguard user experiences.