Anastasia D. Arefyeva
Aim of the study: Digital transformation has affected almost all sectors of the world economy, increasingly and consistently penetrating into information technology, forming a digital environment. At the same time, sociology, which is based on data collection and processing by various population groups, service types, areas of life and other parameters, is no exception.
Methodology: With the growing social digitalization, social media data the volume of which has increased significantly are becoming more and more popular, having concentrated a huge number of participants who give their opinions in networks taking part in public voting on certain issues, thereby leaving the so-called "digital trace." It is the given concept that underlies "Big Data" that are used in sociology enabling to more reliably identify public trends in a particular area. Big Data technology is used in research of virtually any parameters and categories concerning major problems, including health care, education, employment, social sphere, manufacturing and industry, public service delivery, politics and others. Using Big Data simplifies data processing, and makes it more transparent, accurate and reliable as well. Despite the effectiveness of these digital categories, it should be noted that they lose efficiency with no human labor, since they are managed by the research activity.
Conclusion: The article is aimed to identify what new technologies are capable of when conducting sociological research using "Digital traces - Big Data," which is based on the Big Data use. The author concludes that it is Big Data that allows for a better and deeper study in sociology, which has the most reliable results. The theoretical analysis showed how new technologies have been integrated into the sociological research system, while determining their advantages and disadvantages. A sociological labor market study shows how Big Data can be used in sociology. It was concluded that the new technologies enable determining a number of qualitative characteristics of the studied phenomenon, while processing a large array of data that without Big Data cannot be processed