Fake News Identification Based on NLP, Big Data Analysis and Deep Learning Technology

The article describes the study of fake news identification based on Natural Language Processing (NLP), extensive data analysis and deep learning technology. This system automatically checks news for signs of fake news, such as manipulative language, unverified sources and unreliable information. Visualisation of data and result analysis: the user interface that displays the results of news analysis in a convenient and understandable format. The article demonstrates better indicators of news analysis based on Long Short-Term Memory (LSTM) with eight epochs compared to similar works with 3–4 epochs (99% vs 85-96^%) because a neural network was developed for news classification using bidirectional recurrent neural network LSTM (BRNN) and Bidirectional layers in the model. Deep learning models, such as bidirectional LSTM, have high accuracy in recognising patterns in textual data, allowing for better results.