ANALYSIS OF TRAINING METHODS AND NEURAL NETWORK TOOLS FOR FAKE NEWS DETECTION

Abstract

This article analyses various training methods and neural network tools for fake news detection. Approaches to fake news detection based on textual, visual and mixed data are considered, as well as the use of different types of neural networks, such as recurrent neural networks, convolutional neural networks, deep neural networks, generative adversarial networks and others. Also considered are supervised and unsupervised learning methods such as autoencoding neural networks and deep variational autoencoding neural networks. Based on the analysed studies, attention is drawn to the problems associated with limitations in the volume and quality of data, as well as the lack of efficiency of tools for detecting complex types of fakes. The author analyses neural network-based applications and tools and draws conclusions about their effectiveness and suitability for different types of data and fake detection tasks. The study found that machine and deep learning models, as well as adversarial learning methods and special tools for detecting fake media, are effective in detecting fakes. However, the effectiveness and accuracy of these methods and tools can be affected by factors such as data quality, methods used for training and evaluation, and the complexity of the fake media being detected. Based on the analysis of training methods and neural network characteristics, the advantages and disadvantages of fake news detection are identified. Ongoing research and development in this area is crucial to improve the accuracy and reliability of these methods and tools for fake news detection.