Year: 2025
Type: article
Abstract: In the digital age, online reviews significantly influence consumer behavior and business reputation. However, the rise of fake and irrelevant reviews has become a growing concern, affecting the credibility of e-commerce platforms and misleading potential customers. This research presents a comprehensive AI/ML-based system designed to detect and filter out such deceptive content. The system leverages natural language processing (NLP) techniques and a Markov Chain Model to identify gibberish or contextually irrelevant text. It also integrates image verification by analyzing EXIF metadata to validate whether submitted review images are authentic and correspond to the product in question. By combining structured review analysis with machine learning models and metadata validation, the system enhances the reliability of online feedback. This solution not only helps e-commerce platforms maintain content integrity but also builds greater trust among users, ultimately supporting more informed purchasing decisions.
Authors: Meera Sawalkar, T. M. Mane, Rushikesh Kenjale, Tushar Gadekar, Jayprakash Jadhav, Atharva More, Atharva Phadatare
Language: English
Topic: Spam and Phishing Detection
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4411213277