Enhancing Bangla Fake News Detection Using Bidirectional Gated Recurrent
  Units and Deep Learning Techniques
Utsha Roy Mst. Sazia Tahosin Md. Mahedi Hassan Taminul Islam Fahim Imtiaz Md Rezwane Sadik Yassine Maleh Rejwan Bin Sulaiman Md. Simul Hasan Talukder
The rise of fake news has made the need for effective detection methods,
including in languages other than English, increasingly important. The study
aims to address the challenges of Bangla which is considered a less important
language. To this end, a complete dataset containing about 50,000 news items is
proposed. Several deep learning models have been tested on this dataset,
including the bidirectional gated recurrent unit (GRU), the long short-term
memory (LSTM), the 1D convolutional neural network (CNN), and hybrid
architectures. For this research, we assessed the efficacy of the model
utilizing a range of useful measures, including recall, precision, F1 score,
and accuracy. This was done by employing a big application. We carry out
comprehensive trials to show the effectiveness of these models in identifying
bogus news in Bangla, with the Bidirectional GRU model having a stunning
accuracy of 99.16%. Our analysis highlights the importance of dataset balance
and the need for continual improvement efforts to a substantial degree. This
study makes a major contribution to the creation of Bangla fake news detecting
systems with limited resources, thereby setting the stage for future
improvements in the detection process.