A Review of Hadoop Based Frameworks for Fake Review Detection

Online reviews play a critical role in shaping consumer decisions, yet the rapid growth of user generated content has enabled the spread of fake or deceptive reviews. These fraudulent opinions can mislead customers, damage brand credibility, and distort online market trust. This paper reviews existing artificial intelligence AI and machine learning ML methods for detecting fake product feedback and proposes a scalable, Hadoop based framework that integrates text mining, sentiment analysis, and ML classification. The proposed system aims to enhance detection accuracy and computational efficiency for large scale datasets. This study contributes a unified approach that leverages AI and Big Data to safeguard digital marketplaces from deceptive information and restore consumer confidence. Zainab Barwaniwala | Parth Chandna | Abhinav Goud | Dr. Ramesh S. "A Review of Hadoop-Based Frameworks for Fake Review Detection" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-9 | Issue-5 , October 2025, URL: https://www.ijtsrd.com/papers/ijtsrd97585.pdf Paper URL: https://www.ijtsrd.com/computer-science/other/97585/a-review-of-hadoopbased-frameworks-for-fake-review-detection/zainab-barwaniwala