Advantages of Using Multi-Scale Transformers in Detecting Opinion Spam

Online reviews greatly influence consumer purchase decisions, leading to fraudulent behavior as businesses try to improve their online presence or undermine competitors. Transformer models like BERT have become state-of-the-art in detecting fake reviews (opinion spam). This paper compares two multi-head Transformer models utilizing DistilBERT to enhance opinion spam detection: one with a single scale and the other with multi-scale granularity. Our multi-scale model, which includes an additional level of evaluation, achieved an F1 score of 0.9212 and an accuracy of 0.8663 on the YelpZip dataset. These results represent 6% and 11 % improvements over the single-scale model and indicate a promising approach for improving text evaluation in opinion spam detection.