# Bengali Fake Review Detection using Semi-supervised Generative Adversarial Networks

arXiv: https://arxiv.org/abs/2304.02739 · HTML: https://mlsanity.com/paper/2304.02739/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 19 references that are in the map.
- First posted on arXiv 5 Apr 2023.

## Builds on (most cited first, 10 of 10)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [BanglaBERT: Language Model Pretraining and Benchmarks for Low-Resource Language Understanding Evaluation in Bangla](https://mlsanity.com/paper/2101.00204/) (2021)
- [Not Low-Resource Anymore: Aligner Ensembling, Batch Filtering, and New Datasets for Bengali-English Machine Translation](https://mlsanity.com/paper/2009.09359/) (2020)
- [Detecting Deceptive Reviews using Generative Adversarial Networks](https://mlsanity.com/paper/1805.10364/) (2018)
- [Fact or Factitious? Contextualized Opinion Spam Detection](https://mlsanity.com/paper/2010.15296/) (2020)
- [Spam Review Detection Using Deep Learning](https://mlsanity.com/paper/2211.01675/) (2022)
- [Leveraging GPT-2 for Classifying Spam Reviews with Limited Labeled Data via Adversarial Training](https://mlsanity.com/paper/2012.13400/) (2020)

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

- [Bengali Intent Classification with Generative Adversarial BERT](https://mlsanity.com/paper/2312.10679/) (2023)
- [Detecting Racist Text in Bengali: An Ensemble Deep Learning Framework](https://mlsanity.com/paper/2401.16748/) (2024)
- [A Survey on Fake Review Detection: From Pre-trained Language Models to Large Language Models](https://mlsanity.com/paper/2609.30292/) (2026)

## Abstract (arXiv)

This paper investigates the potential of semi-supervised Generative Adversarial Networks (GANs) to fine-tune pretrained language models in order to classify Bengali fake reviews from real reviews with a few annotated data. With the rise of social media and e-commerce, the ability to detect fake or deceptive reviews is becoming increasingly important in order to protect consumers from being misled by false information. Any machine learning model will have trouble identifying a fake review, especially for a low resource language like Bengali. We have demonstrated that the proposed semi-supervised GAN-LM architecture (generative adversarial network on top of a pretrained language model) is a viable solution in classifying Bengali fake reviews as the experimental results suggest that even with only 1024 annotated samples, BanglaBERT with semi-supervised GAN (SSGAN) achieved an accuracy of 83.59% and a f1-score of 84.89% outperforming other pretrained language models - BanglaBERT generator, Bangla BERT Base and Bangla-Electra by almost 3%, 4% and 10% respectively in terms of accuracy. The experiments were conducted on a manually labeled food review dataset consisting of total 6014 real and fake reviews collected from various social media groups. Researchers that are experiencing difficulty recognizing not just fake reviews but other classification issues owing to a lack of labeled data may find a solution in our proposed methodology.