Distributed Fake Review Detection and Real-Time Anomaly Detection: A Technical Framework

Abstract

This work presents a distributed real-time system for detecting fake reviews on digital platforms, addressing growing challenges to marketplace integrity. Our architecture combines event-driven streaming pipelines (Apache Flink, Kafka Streams, and Spark Streaming) with advanced machine learning to process reviews instantly, enabling detection within 100 milliseconds. The system integrates natural language processing, graph neural networks, and behavioral analytics to identify complex fraud patterns such as bot-generated content, collusive reviewer networks, and coordinated campaigns. A hybrid anomaly detection model evaluates sentiment consistency, user behavior, and temporal bursts, achieving a precision of 0.94 across diverse fraud types. To support privacy and scalability, we incorporate federated learning with differential privacy, maintaining an F1 score above 0.92 (ε = 4.6) while reducing data exposure by 97%. Evaluations on large-scale datasets demonstrate low-latency, high-precision detection adaptable to evolving tactics, enhancing trust and security in online review ecosystems.