Deep learning models for effective detection of fake news

This work is devoted to the development of algorithms aimed at improving the detection of false and malicious information disseminated under the guise of news reports. Special attention is paid to their impact on the reliability of information in the modern digital world. To classify and identify fake news, deep learning algorithms have been proposed, including bidirectional recurrent neural networks (BiLSTM), recurrent neural networks (LSTM) and convolutional neural networks followed by bidirectional recurrent networks (CNN + BiLSTM). The effectiveness of the developed models was evaluated on a specialized dataset containing the texts of real and fake news articles in English, taking into account the news agenda in the Islamic context. The results showed that the BiLSTM model achieved a slightly higher accuracy of 98.0% compared to other models, which proves the effectiveness of deep learning algorithms in detecting fake news. The study offers a promising approach to solving this problem and highlights the importance of developing better tools to ensure the accuracy of information and protect users from misinformation. In the conducted studies, by improving the neural network configuration, it became possible to increase the accuracy of the LSTM, BiLSTM, CNN + BiLSTM models by 7.65, 2.5 and 1.9%, respectively, compared with previous results.