Abstract:
Identification of Bangla Fake News is very challenging, mostly when there is so much
news from multiple online sources like social media, online news, and other online
platforms. Detecting fake news is comparable in significance to eliminating crime from
society. In this era of digital communication, the ease of spreading violence and
manipulating public perception has reached unprecedented levels. Genuine news is
frequently distorted into fake narratives for personal motives. In our daily lives, where we
extensively utilize online platforms for various purposes, the relentless bombardment of
fake news has become pervasive. To safeguard our well-being, it is imperative to put an
end to this toxicity, and the key to achieving this lies in the detection of fake news.
My research is centered on addressing this crucial aspect. I endeavored to construct a
framework for detecting false news, utilizing a significant dataset consisting of Bengali
news information sourced from diverse online outlets. Employing both approaches in
machine learning and as well as deep learning, this study revealed notable outcomes. In
this study we used models such as Bidirectional GRU Model, Convolutional Neural
Network (CNN) + Long Short-Term Memory (LSTM) + GlobalMaxPooling1D layers
(Hybrid Model), Long Short-Term Memory (LSTM), and 1D Convolutional Neural
Network (CNN). The Bidirectional GRU Model exhibited the highest accuracy of 99.13%.
Despite facing limitations due to the performance constraints of our device, our research
lays the foundation for developing an application dedicated to distinguishing between fake
and authentic news.
