EDUCATIONAL MIGRATION IN SIBERIA: RESEARCH BASED ON SOCIAL NETWORK DATA

Abstract

The prevalence of social networking services and the possibility of extracting data in a generalized form from user profiles make it possible to significantly supplement official statistics. In this article, we contribute to the development of approaches that use social media information to study migration associated with higher education. The subject of the work is internal Russian educational migrations in Siberia. We aim to study the migration routes of applicants to Siberian universities as well as to regionalize the territory of Siberia in relation to the cities being university centers. For the study, user profiles were downloaded from the most commonly used social network in Russia, VKontakte. Basing on the correlation of information about the places of birth or school graduation with information about institutions of higher education indicated by users, we analyzed the territorial structure of the places of origin of 484.6 thousand users who are receiving or received higher education in Siberian universities in the past. As part of the study, we determined the gravity zones of Siberian cities being centers of higher education. The boundaries of such zones basically coincide with the boundaries of the constituent entities of the Russian Federation. However, in some Siberian regions, outlying municipalities fall into the gravity zone of the administrative centers of neighboring entities. Tomsk is distinguished by the widest geographical representation of the applicants. In this city, judging by the data of VKontakte, natives and school graduates from other regions of the Russian Federation prevail over immigrants from the Tomsk region. In addition, 15 intra-regional city centers were identified, whose natives and school graduates preferred to receive higher education in local universities, rather than in the ‘capitals’ of the constituent entities of the Russian Federation. There are such centers in 5 Siberian regions, the largest of them is Novokuznetsk. The results show the possibilities of using social network data to study and improve regional education policy.