Enhancing Bangla Fake News Detection Using Bidirectional Gated Recurrent Units and Deep Learning Techniques

核心概念

The study aims to address the challenges of Bangla fake news detection using deep learning models, including the bidirectional Gated Recurrent Unit (GRU), and achieve high accuracy in identifying authentic and fake information.

要約

The study focuses on enhancing Bangla fake news detection using deep learning techniques. It proposes a complete dataset of around 50,000 news items and tests several deep learning models, including the bidirectional Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), 1D Convolutional Neural Network (CNN), and hybrid architectures. The study evaluates the models using various performance metrics, such as recall, precision, F1 score, and accuracy. The bidirectional GRU model achieves an impressive accuracy of 99.16% after addressing the issue of class imbalance. The study highlights the importance of dataset balance and the need for continuous improvement efforts to enhance the detection process. It contributes to the development of Bangla fake news detection systems with limited resources, paving the way for future advancements.

統計

The dataset contains around 50,000 news items, including 48,000 authentic news articles and 1,000 fake news articles.

引用

"The rise of fake news has made the need for effective detection methods, including in languages other than English, increasingly important."
"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."

抽出されたキーインサイト

Enhancing Bangla Fake News Detection Using Bidirectional Gated Recurrent Units and Deep Learning Techniques

by Utsha Roy,Ms... 場所 arxiv.org 04-03-2024

https://arxiv.org/pdf/2404.01345.pdf

深掘り質問

How can the proposed models be extended to detect fake news in other low-resource languages?

The proposed models can be extended to detect fake news in other low-resource languages by following a similar methodology of creating a comprehensive dataset, preprocessing the data, and implementing deep learning models. The key steps would involve collecting a substantial amount of news articles in the target language, cleaning and preparing the data, tokenizing and padding the text for numerical representation, and training models like 1D CNN, LSTM, and hybrid architectures. By adapting the dataset creation and model training process to the linguistic nuances of the specific language, the models can be fine-tuned to effectively detect fake news in other low-resource languages.

What are the potential challenges in applying the bidirectional GRU model to real-time Bangla news streams, and how can they be addressed?

One potential challenge in applying the bidirectional GRU model to real-time Bangla news streams is the computational complexity and resource requirements of processing a continuous stream of data in real-time. The bidirectional nature of the GRU model may introduce delays in processing new information, impacting the real-time aspect. To address this challenge, optimizations such as parallel processing, efficient data streaming techniques, and model compression can be implemented to reduce the computational burden and ensure timely analysis of incoming news data. Additionally, implementing a robust data pipeline and optimizing the model architecture for speed and efficiency can help overcome the challenges of real-time processing.

What insights can be gained by analyzing the linguistic and contextual features that contribute to the high performance of the bidirectional GRU model in Bangla fake news detection?

Analyzing the linguistic and contextual features that contribute to the high performance of the bidirectional GRU model in Bangla fake news detection can provide valuable insights into the language-specific characteristics of fake news. By examining the patterns, idiomatic expressions, and cultural nuances present in the dataset, researchers can gain a deeper understanding of how fake news is structured and disseminated in the Bangla language. This analysis can reveal common linguistic markers of fake news, helping to improve the model's accuracy and identifying unique features that distinguish authentic news from fake news. Additionally, insights into the contextual cues and language usage in fake news articles can inform the development of more sophisticated detection algorithms tailored to the specific linguistic landscape of Bangla.