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RESEARCH PAPER
A Complex Neural Network Model for Predicting a Personal Success based on their Activity in Social Networks
1
Kazan (Volga region) Federal University, 420008, Kremlevskaya 18, Kazan, RUSSIA
2
I.M. Sechenov First Moscow State Medical University (Sechenov University), 119991, Trubetskaya Street 8, Moscow, RUSSIA
3
Financial University under the Government of the Russian Federation, 125993, Leningradsky Prospect 49, Moscow, RUSSIA
Publication date: 2021-08-24
EURASIA J. Math., Sci Tech. Ed 2021;17(10):em2010
KEYWORDS
ABSTRACT
The development and improvement of effective tools for predicting human behavior in real life
through the features of its virtual activity opens up broad prospects for psychological support of
the individual. The presence of such tools can be used by psychologists in educational,
professional and other areas in the formation of trajectories of harmonious person’s development.
Currently, active research is underway to determine psychological characteristics based on
publicly available data. Such studies develop the direction of “Psychology of social networks”. As
markers for determining the psychological characteristics of people, various parameters obtained
from their personal pages in social networks are used (texts of posts and reposts, the number of
different elements on the page, statistical information about audio and video recordings,
information about groups, and others). There is a difficulty in obtaining and analyzing a data set
this big, as there are non-linear and hidden relationships between individual data elements. As a
result, the classic methods of information processing become inefficient. Therefore, in our work
to develop a comprehensive model of success based on the analysis of qualitative and
quantitative data, we use an approach based on artificial neural networks. The labels of the input
records are used to divide the subjects of the study into five clusters using clustering methods (k-means). In the course of our work, we gradually expand the set of input parameters to include
metrics of users’ personal pages, and compare the results to determine the impact of qualitative
parameters on the accuracy of the artificial neural network. The results reflect the solution of one
of the tasks of the research carried out within the framework of the project of the Russian Science
Foundation and serve as material for an information and analytical system for automatic
forecasting of human life activity based on the metrics of his personal profile in the social network
VKontakte.
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