Bengali Fake Review Detection using Semi-supervised Generative Adversarial Networks

• URL: http://arxiv.org/abs/2304.02739v1
• Date: Wed, 5 Apr 2023 20:40:09 GMT
• Title: Bengali Fake Review Detection using Semi-supervised Generative Adversarial Networks
• Authors: Md. Tanvir Rouf Shawon, G. M. Shahariar, Faisal Muhammad Shah, Mohammad Shafiul Alam and Md. Shahriar Mahbub
• Abstract summary: This paper investigates the potential of semi-supervised Generative Adversarial Networks (GANs) to fine-tune pretrained language models. We have demonstrated that the proposed semi-supervised GAN-LM architecture is a viable solution in classifying Bengali fake reviews.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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