Scalable Fake Review Detection: Leveraging Machine Learning for Trustworthy Online Platforms

Online fake review proliferation has drastically influenced market dynamics and consumer trust. Existing rule-based systems fail to effectively scale up against intelligent forms of fraud. The paper here provides a scalable machine learning framework for detecting fake reviews based on natural language processing, sentiment analysis, and deep neural networks. The system evaluates linguistic features, reviewer behavior, and metadata discrepancies in detecting malicious content. Through rigorous testing on actual datasets, the system exhibits high precision and flexibility in different e-commerce and social media platforms. The research emphasizes the need for scalable AI-based solutions in upholding platform integrity and providing stable user experiences.