Fake Reviews Detection on E-Commerce Websites Using Novel User Behavioral Features: An Experimental Study

The trend of writing fake reviews has recently increased with the rapid growth of e-commerce websites. Fake reviews are usually written to promote or demote the targeted products to affect the customer's decision and thus achieve a competitive advantage. Several techniques have been proposed to detect fake reviews written in English, and promising results have been obtained in the literature. Nevertheless, detecting fake reviews for low-resource languages (such as Roman Urdu) is still in the infancy stage and suffers from low classification results for two main reasons. Firstly, the existing studies mostly worked on textual features or lingual features. Secondly, the datasets used in existing studies are highly imbalanced, and proper attention to this issue may further enhance the performance. Therefore, to address these weaknesses and further enhance the performance, we have identified three types of discriminative features: review textual features, review lingual features, and review behavioral features using the Daraz 1 dataset. Moreover, we evaluated LSTM-based text generation techniques for textual features and the random undersampling and oversampling for behavioral and lingual features to deal with class imbalance problems. Finally, we empirically evaluated the performance of machine learning and deep learning algorithms in classifying fake reviews written in the Roman Urdu language. The experimental results show that user behavioral features play a vital role in detecting fake reviews. Moreover, it was found that text generation is ineffective for balancing the textual data because the informative feature for fake review detection depends on the user behavioral features compared to textual features. Finally, the experimental results show that gradient boosting (GB) outperformed other models and improved 3% accuracy from the baseline study.