Data Augmentation for Fake Reviews Detection in Multiple Languages and Multiple Domains
This addresses the data scarcity problem for fake review detection systems in multiple languages and domains, though it appears incremental as it applies existing data augmentation techniques to this specific task.
The researchers tackled the problem of limited training data for fake review detection in low-resource languages and domains by using large language models to generate augmented datasets, resulting in accuracy improvements of 0.3 to 10.9 percentage points across multiple test sets.
With the growth of the Internet, buying habits have changed, and customers have become more dependent on the online opinions of other customers to guide their purchases. Identifying fake reviews thus became an important area for Natural Language Processing (NLP) research. However, developing high-performance NLP models depends on the availability of large amounts of training data, which are often not available for low-resource languages or domains. In this research, we used large language models to generate datasets to train fake review detectors. Our approach was used to generate fake reviews in different domains (book reviews, restaurant reviews, and hotel reviews) and different languages (English and Chinese). Our results demonstrate that our data augmentation techniques result in improved performance at fake review detection for all domains and languages. The accuracy of our fake review detection model can be improved by 0.3 percentage points on DeRev TEST, 10.9 percentage points on Amazon TEST, 8.3 percentage points on Yelp TEST and 7.2 percentage points on DianPing TEST using the augmented datasets.