NoFake Trustworthy Reviewer Scoring System for Detecting Fake Reviews

Abstract

The proliferation of fake online reviews has severely undermined consumer trust and market fairness in digital platforms. Traditional detection systems relying on rule-based filters or shallow machine learning models often fail to identify sophisticated, artificially intelligent-generated, or behaviorally deceptive content. This research proposes an intelligent system for fake review detection that integrates advanced Natural Language Processing, behavioral anomaly analysis, and explainable artificial intelligence to enhance accuracy, transparency, and usability. The system employs a hybrid feature set of about 212 dimensions, including Term Frequency-Inverse Document Frequency scores, sentiment-rating inconsistency, readability metrics, and reviewer behavioral indicators such as review frequency and account metadata. A neural network trained on the Deceptive Opinion Spam dataset (40,000 reviews) classifies reviews as genuine or fake, achieving 81.55% accuracy and an Area Under the Receiver Operating Characteristic Curve of 0.9019. Anomaly detection models Isolation Forest generate a dynamic trust score for reviewers, improving detection of coordinated spam campaigns. To ensure transparency, SHapley Additive exPlanations are integrated into a full-stack web application, providing human-readable insights such as Sentiment-Rating Mismatch or Robotic Writing Detected. Results show the proposed system outperforms traditional methods in accuracy and interpretability, offering a scalable and trustworthy solution for digital trust ecosystems.