Fake News Detection With Deep Neural Networks

Résumé: The proliferation of fake news has become a significant concern in today's digital age, where information spreads rapidly through various online platforms. This search presents a comprehensive study on the detection of fake news using deep neural networks (DNNs). We explore the application of advanced deep learning techniques, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and transformer-based models, to identify and classify fake news. Our approach leverages large datasets to train the models, ensuring robustness and accuracy. By incorporating natural language processing (NLP) techniques, such as word embeddings and attention mechanisms, we enhance the models' ability to understand and analyze the context and semantics of news articles. Experimental results demonstrate that our deep learning models outperform traditional machine learning methods, achieving high accuracy in distinguishing between genuine and fake news. The findings underscore the potential of deep neural networks as effective tools in combating misinformation and highlight the importance of ongoing research in this domain.

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