# Data Augmentation for Fake Reviews Detection in Multiple Languages and Multiple Domains

arXiv: https://arxiv.org/abs/2504.06917 · HTML: https://mlsanity.com/paper/2504.06917/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 71 references that are in the map.
- First posted on arXiv 9 Apr 2025.

## Builds on (most cited first, 11 of 11)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [OPT: Open Pre-trained Transformer Language Models](https://mlsanity.com/paper/2205.01068/) (2022)
- [A Survey of Data Augmentation Approaches for NLP](https://mlsanity.com/paper/2105.03075/) (2021)
- [A Survey on Data Augmentation for Text Classification](https://mlsanity.com/paper/2107.03158/) (2021)
- [Data Augmentation Approaches in Natural Language Processing: A Survey](https://mlsanity.com/paper/2110.01852/) (2021)
- [A Unified Neural Coherence Model](https://mlsanity.com/paper/1909.00349/) (2019)
- [Narrative Interpolation for Generating and Understanding Stories](https://mlsanity.com/paper/2008.07466/) (2020)
- [Detecting Deceptive Reviews using Generative Adversarial Networks](https://mlsanity.com/paper/1805.10364/) (2018)
- [The use of Data Augmentation as a technique for improving neural network accuracy in detecting fake news about COVID-19](https://mlsanity.com/paper/2205.00452/) (2022)
- [ScoreGAN: A Fraud Review Detector based on Multi Task Learning of Regulated GAN with Data Augmentation](https://mlsanity.com/paper/2006.06561/) (2020)
- [Wisdom of the Crowd or Wisdom of a Few? An Analysis of Users' Content Generation](https://mlsanity.com/paper/1604.03044/) (2016)

## Built on by (most cited first, 1 of 1)

- [A Survey on Fake Review Detection: From Pre-trained Language Models to Large Language Models](https://mlsanity.com/paper/2609.30292/) (2026)

## Abstract (arXiv)

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.