Summary of Enhancing Bangla Fake News Detection Using Bidirectional Gated Recurrent Units and Deep Learning Techniques, by Utsha Roy et al.
Enhancing Bangla Fake News Detection Using Bidirectional Gated Recurrent Units and Deep Learning Techniques
by Utsha Roy, Mst. Sazia Tahosin, Md. Mahedi Hassan, Taminul Islam, Fahim Imtiaz, Md Rezwane Sadik, Yassine Maleh, Rejwan Bin Sulaiman, Md. Simul Hasan Talukder
First submitted to arxiv on: 31 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed study aims to address the challenges of fake news detection in Bangla, a language often considered less important than others. To this end, a comprehensive dataset containing approximately 50,000 news items is introduced. Several deep learning models are evaluated on this dataset, including bidirectional gated recurrent units (GRUs), long short-term memories (LSTMs), one-dimensional convolutional neural networks (CNNs), and hybrid architectures. The efficacy of these models is assessed using recall, precision, F1 score, and accuracy measures. Notably, the Bidirectional GRU model achieves an impressive accuracy of 99.16%. The study highlights the importance of dataset balance and emphasizes the need for ongoing improvement efforts to achieve substantial results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study helps us detect fake news in Bangla more effectively. To do this, researchers created a big dataset with about 50,000 news articles. They tested different deep learning models on this data to see which ones work best. The models they tried include some that are really good at recognizing patterns and others that can learn from mistakes. They even found one model that’s almost perfect at detecting fake news – it gets it right 99.16% of the time! This study shows us why having a balanced dataset is important and how we can keep improving our fake news detection systems. |
Keywords
» Artificial intelligence » Deep learning » F1 score » Precision » Recall
