Abstract
Due to the popularity of social networks, all famous brands use them to promote and support their product. For example, well-known foreign newspapers such as: Washington Post, New York Times, Time, Reuters, Forbes duplicate information about news on the Facebook social network for greater readership. Using methods of automated data collection from web pages, a list of posts is formed based on which data analysis is performed. Statistical results of frequency, popularity of certain articles, audience reach and people's reaction to posts are obtained from a large volume of data. In the Java programming language and using additional Selenium, JavaFX libraries, all processes for data normalization is developed and data visualization is used. In addition, the dependence of the post coverage of newspaper editions on the number of posts published during the day is investigated in Facebook social network. The work also examines the most popular posts and their topics. The relationship between keywords and real events is analyzed. Keywords : big data, social networks, data analytics, automated data collection, data processing.
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