Combating Deepfake News in Bengali: Detection and Prevention of Manipulated Social Media’s Content Using Machine Learning

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dc.contributor.authorMD. JANE ALAM
dc.date.accessioned2025-11-23T04:11:09Z
dc.date.available2025-11-23T04:11:09Z
dc.date.issued2024-03-30
dc.description.abstractWith 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
dc.identifier.urihttps://dspace.nstu.ac.bd/handle/123456789/109
dc.titleCombating Deepfake News in Bengali: Detection and Prevention of Manipulated Social Media’s Content Using Machine Learning

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