A Product Review Spammer Detection Model Based on Hybrid PU-Learning

The widespread manipulation of online product reviews by spammers has emerged as one of the most pressing integrity challenges facing modern e-commerce platforms. Conventional spam detection approaches that rely on fixed feature sets and standard classifiers are increasingly outpaced by a new generation of crowdsourced spammers who systematically mimic the behavioral patterns of genuine users. To address this growing threat, this paper proposes hPSD, a Hybrid Positive-Unlabeled Learning-Based Spammer Detection model that jointly leverages individual user behavioral features and the structural information embedded in user-product relational networks. Operating under a semi-supervised framework, hPSD begins with a small seed set of confirmed spammers and iteratively refines its detection through a reliable negative extraction algorithm and a Bayesian hybrid classifier. The model is capable of identifying multiple distinct types of spammers in a single unified pipeline. Extensive experiments on both a synthetic movie-review dataset with injected shilling attacks and a real-world Amazon review corpus demonstrate that hPSD significantly outperforms eight state-of-the-art baseline detectors, achieving a precision of 0.81, recall of 0.97, and F-measure of 0.90 on real-world data. The framework additionally uncovers hidden employer organizations coordinating spammer activity, demonstrating practical value beyond standard detection metrics.