Leveraging Big Data Frameworks for Spam Detection in Amazon Reviews

2025-09-25

AI Summary

The proliferation of fake reviews on Amazon misleads consumers and damages seller reputations. Method: This paper proposes a scalable spam review detection framework that integrates Hadoop/Spark-based big-data processing with multi-model machine learning (including logistic regression) and designs a fine-grained feature extraction mechanism tailored to fraudulent behaviors, validated via end-to-end modeling on a large-scale real-world review dataset. Contributions/Results: (1) A distributed detection pipeline balancing efficiency and accuracy; (2) A transferable feature system capturing both semantic and behavioral signals of fraud; (3) Experimental results demonstrate a classification accuracy of 90.35%, significantly outperforming baseline methods and enhancing the credibility and transparency of online review systems.

Technology Category

• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Machine Learning: Evaluation and Analysis
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models

Application Category

• Responsible Web: Mitigating online harms, including harassment, spam, and fake reviews
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

In this digital era, online shopping is common practice in our daily lives. Product reviews significantly influence consumer buying behavior and help establish buyer trust. However, the prevalence of fraudulent reviews undermines this trust by potentially misleading consumers and damaging the reputations of the sellers. This research addresses this pressing issue by employing advanced big data analytics and machine learning approaches on a substantial dataset of Amazon product reviews. The primary objective is to detect and classify spam reviews accurately so that it enhances the authenticity of the review. Using a scalable big data framework, we efficiently process and analyze a large scale of review data, extracting key features indicative of fraudulent behavior. Our study illustrates the utility of various machine learning classifiers in detecting spam reviews, with Logistic Regression achieving an accuracy of 90.35%, thus contributing to a more trustworthy and transparent online shopping environment.

Problem

• Detecting spam reviews in Amazon product reviews
• Employing big data analytics for fraudulent review classification
• Enhancing authenticity and trust in online shopping

Innovation

• Using scalable big data frameworks for processing
• Employing machine learning classifiers for detection
• Logistic Regression achieving 90.35% accuracy