Combating Deepfake News in Bengali: Detection and Prevention of Manipulated Social Media’s Content Using Machine Learning
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Date
2024-03-30
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Abstract
With the rapid growth of digital media and social networks, the spread of misinformation and
manipulated content has become a significant concern. Nowadays, misinformation and false
rumors mostly come from social media platforms. Deepfake technology, which allows the creation
of realistic but fabricated news, audio, images, and videos, has emerged as a potent tool for the
production and dissemination of deceptive content. While the detrimental impacts of deepfake
news are widely recognized, research efforts in combating this phenomenon are largely focused
on English and a few other major languages. On the other hand, Bengali grammar and language
are significantly more difficult and essential to learn. This research paper aims to address this gap
by proposing a comprehensive approach for detecting and preventing deepfake news specifically
in the Bengali language.
This study utilizes a newly released set of Bangla fake news dataset, marked by experts. It then
employs Bengali-based embeddings for machine learning classifiers and utilizes trained
bidirectional encoder representations from transformers (BERT) in Bengali to determine
sentiments in Bengali grammar.
The results show that increasing the training data consistently enhanced the BERT-based
classifiers' performance, surpassing that of ML classifiers. When compared to prior studies on
detecting Bengali fake news, the current study's findings indicate a more significant improvement.
Keyword: Bangla Fake News, BERT classifier, Fake News Detection, Bangla