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.
Highlights
In this work we present an interdisciplinary research carried out at the junction of two subject areas: psychology and information systems
This study provides a generalized system for predicting the success of an individual through the features of his professional activity via social network activity metrics
The results contribute to the understanding of the processes of interaction between the “Self-Real” and the “Self-Virtual” and contribute to the research of the psychology of social networks
Summary
In this work we present an interdisciplinary research carried out at the junction of two subject areas: psychology and information systems. It is a variety of digital content that a person leaves in the form of posts, audio and video plots, photos, etc The complexity of such studies lies in the need to collect and analyse a large amount of data from social networks, and because of the presence of nonlinear relationships between the behavioural characteristics of a person in real life and indicators of his virtual activity (Du et al, 2018; Galchenko et al, 2020; Tugun et al, 2020). This results on the low efficiency of traditional methods of data processing and analysis. One of the solutions is the possibility of using machine learning methods
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Eurasia Journal of Mathematics, Science and Technology Education
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
