# Leveraging GPT-2 for Classifying Spam Reviews with Limited Labeled Data via Adversarial Training

arXiv: https://arxiv.org/abs/2012.13400 · HTML: https://mlsanity.com/paper/2012.13400/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Dec 2020.

## Built on by (most cited first, 3 of 3)

- [Improve Learning from Crowds via Generative Augmentation](https://mlsanity.com/paper/2107.10449/) (2021)
- [Combat AI With AI: Counteract Machine-Generated Fake Restaurant Reviews on Social Media](https://mlsanity.com/paper/2302.07731/) (2023)
- [Bengali Fake Review Detection using Semi-supervised Generative Adversarial Networks](https://mlsanity.com/paper/2304.02739/) (2023)

## Abstract (arXiv)

Online reviews are a vital source of information when purchasing a service or a product. Opinion spammers manipulate these reviews, deliberately altering the overall perception of the service. Though there exists a corpus of online reviews, only a few have been labeled as spam or non-spam, making it difficult to train spam detection models. We propose an adversarial training mechanism leveraging the capabilities of Generative Pre-Training 2 (GPT-2) for classifying opinion spam with limited labeled data and a large set of unlabeled data. Experiments on TripAdvisor and YelpZip datasets show that the proposed model outperforms state-of-the-art techniques by at least 7% in terms of accuracy when labeled data is limited. The proposed model can also generate synthetic spam/non-spam reviews with reasonable perplexity, thereby, providing additional labeled data during training.