A Predictive Framework for Identifying Fake Reviews on E-Commerce Platforms

Fake reviews in particular are degrading the trust between e-commerce and consumers, because theycan mislead them and misrepresent about the products. To determine this, we propose a hybrid fakereview detection framework with both the linguistic and behavioural signals. A distilled transformerencoder produces first-order rich-bidirectional contextual representations of each review so as tocapture subtle semantic irregularities. These embeddings are then analyzed by a bidirectional LSTMin order to understand long-range sequential dependencies and an attention mechanism is used tohighlight the most deception-worthy words and phrases. In parallel, teaching behaviour features at ascale that is representative of the structure of reviews and statistics of the scripts are fused with deeptextual representation, allowing the model to make use of stylistic anomalies alongside the extractionof contextual meaning, making far more reliable authenticity prediction possible. The experiments onthe Amazon Enhanced Fake Reviews Dataset show the proposed model has an accuracy of 99.85%,99.87% precision, 99.84% recall, and a 99.85% F1-score, which is evenly better than strong baselineproposals. These results validate that the joint utilization of transformer-level semantics, sequentialdynamics, attention-guided saliency, and behavioral evidence is a highly reliable solution to detectfraudulent opinion spam, hence the introduced framework is ready to be used in practice in real onlinemarketplaces for preserving the authenticity of reviews and protecting consumers from misleadingcontents.