fake news

The article describes a study of identification of fake news based on natural language processing, big data analysis and deep learning technology. The developed system automatically checks the news for signs of fake news, such as the use of manipulative language, unverified sources and unreliable information. Data visualization is implemented on the basis of a friendly user interface that displays the results of news analysis in a convenient and understandable format.

The article analyzes the Ukrainian experience of using media mystification in the content of all-Ukrainian TV channels. An actual problem is the use of the genre of hoax in the content of all-Ukrainian TV channels, with the help of which journalists demonstrate fake news or fabricated programs on television as real in the country and abroad. The scientific approaches of scientists to the definition of the concept of "media mystification" are analyzed.

The article describes the characteristics of “fake”, “fake news”. The manipulative technologies in the media are considered. According to the author, today the users of social networks constantly see false information, written and disseminated in order to increase internet traffic.

2016 was the year of two significant political campaigns, accompanying the referendum on membership of the United Kingdom in the European Union and presidential election in the USA. A popular image of these both campaigns consists of many cases of twisting the truth by the winning parties. It was confirmed by announcing the terms ’fake news’ and ’post-truth’ as Words of the Year 2016. Both words were and are widely used by politicians, journalists and voters. This article is an attempt to prove if these both new words actually mean new phenomena in journalism.