Rapid detection of fake news based on machine learning methods

Nowadays, it is very important to quickly recognize the false information referred to as fake news. This is especially important in the case of news appearing on the Internet because of its wide and rapid spreading. It is equally important to be able to initially classify news as fake or true based on the title itself. In this paper, we propose an approach to classifying news based on the title without analyzing the other aspects. The obtained results will be compared with classification based on the whole news text. The goal of this work is to propose a method that balances between data analysis time and quality of classification in fake news prediction. We use natural language processing (NLP) to describe the title and text of the news. This is a complex process, requiring good analysis to be applied to classification. Therefore, the use of complex classifiers - in this case, classical ensemble methods - has been proposed in order to achieve a high quality of classification (measured by popular measure). In this paper, we present analyses of a real data set and results of news classification using the proposed model - including an ensemble of classifiers and single classifiers. (C) 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://crativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of KES International.