Fake Review Detection

Fake review detection aims to identify fraudulent online reviews that manipulate consumer opinions and impact e-commerce. Current research heavily utilizes deep learning models, particularly transformer architectures like BERT and XLNet, often enhanced with techniques like contrastive learning and semi-supervised GANs to improve accuracy and address the challenges posed by diverse review styles and languages. This field is crucial for maintaining the integrity of online marketplaces and protecting consumers from deceptive practices, with ongoing efforts focused on improving model explainability and expanding detection capabilities to multilingual and multimodal (text and image) data.