Parallel Computing in Problems of Classification of Teenagers Based on Analysis of Digital Traces

This paper considers a model for classifying high school students by digital traces obtained from the VKontakte social network. The classification is based on the belonging of social network users to communities, the number of which is about hundreds of thousands, which leads to the emergence of big data in the process of analysis. The problem of working with big data is solved by parallelizing computations. The classification model was developed with the aim of recovering information from digital traces of users of social networks. On the basis of the trained model, the identification of users of the VKontakte social network was carried out by place of residence (village or city of the Altai Territory) and age (9 or 11 grade) among teenagers with incomplete information on the grade and place of study in the digital traces. The best prediction accuracy for the trained model was of the order of 0.9. In the future, it is planned to build an extended classification model by including in the data sample of users of social networks of other age groups and to develop a support system for making managerial decisions for the university's admissions campaign.