This paper introduces the Bengali Fake Review Detection (BFRD) dataset, the first publicly available dataset for identifying fake reviews in Bengali. The dataset consists of 7710 non-fake and 1339 fake food-related reviews collected from social media posts. To convert non-Bengali words in a review a unique pipeline has been proposed that translates English words to their corresponding Bengali meaning and also back transliterates Romanized Bengali to Bengali. We have conducted rigorous experimentation using multiple deep learning and pre-trained transformer language models to develop a reliable detection system. Finally, we propose a weighted ensemble model that combines four pre-trained transformers: BanglaBERT, BanglaBERT Base, BanglaBERT Large and BanglaBERT Generator.

The paper "Bengali Fake Reviews: A Benchmark Dataset and Detection System" accepted in Neuroomputing, a journal published by Elsevier.

The repository has two folders:

Code: All the codes for deep learning models, transformers, ensemble model and text conversion pipeline are available. Dataset: Contains two excel files (a) fake.xlsx (b) non-fake xlsx Each file contains two columns: Review (collected raw reviews), Label (annotations).

• Annotated by 4 native Bangla speakers with more than 90% trustworthiness score.

• Fleiss' Kappa Score: 0.83

• Fake - 1339
• Non-fake - 7710

If you use the datasets, please cite the following paper:

@article{shahariar2023bengali,
  title={Bengali Fake Reviews: A Benchmark Dataset and Detection System},
  author={Shahariar, GM and Shawon, Md Tanvir Rouf and Shah, Faisal Muhammad and Alam, Mohammad Shafiul and Mahbub, Md Shahriar},
  journal={arXiv preprint arXiv:2308.01987},
  year={2023}
}