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COMPUTING SYSTEMS AND NETWORKS
A. G. Abramov, A. A. Gonchar, A. V. Evseev, B. M. Shabanov "Main Results of the First Stages of the Project for the Development of National Research Computer Network"
V. S. Ivashko, E. E. Ivashko Volunteer Computing Projects in Citizen Science: Dynamics and Statistics
DATA PROCESSING AND ANALYSIS
A. V. Osipov, A. E. Sapozhnikov, E. S. Pleshakova, S. T. Gataullin Machine Learning Methods for Recognizing the Emotional State of a Telecommunications System Subscriber
R. A. Mikailova, A. Yu. Shesterikov, S. I. Spiridonov, S. V. Fesenko Electronic Library of Applied Radioecological Models
A. R. Teplyakova Development of a Module for Determining the Size and Volume of Pulmonary Nodules
G. E. Ryazantsev, V. D. Oliseenko, M. V. Abramov, T. V. Tulupyeva Predicting the Results of the R. Cattell Test Based on the Social Network User Profiles
INTELLIGENCE SYSTEMS AND TECHNOLOGIES
A. A. Zatsarinny, A. A. Karandeev, A. E. Maslov, V. P. Osipov, N. Y. Apalkov Development of Technologies Based on Additional Properties
M. Y. Kataev, A. B. Kurakov Comparative Analysis of Methods for Calculating the Center of Gravity in the Task of Analyzing Human Movement
V. A. Fedorenko, K. O. Sorokina, P. V. Giverts The Classification of Firing Pin Impressions Using the Convolutional Neural Network (CNN)
S. A. Slastnikov, L. F. Zhukova, I. V. Semichasnov Application for Data Retrieval, Analysis, and Forecasting in Social Networks
MATHEMATICAL FOUNDATIONS OF INFORMATION TECHNOLOGY
S. A. Inyutin A method for Calculating the Positional Characteristics of a Modular Representation with Linear Complexity
G. E. Ryazantsev, V. D. Oliseenko, M. V. Abramov, T. V. Tulupyeva Predicting the Results of the R. Cattell Test Based on the Social Network User Profiles
Abstract. 

Digital footprints of users in the social network and the results of passing the 16-factor R. Cattell test. The method consists in applying statistical methods and relevant machine learning algorithms to personal data on the user's page. The main results of the experiment are the identification of a significant correlation between the factors evaluated by the R. Cattell test and digital footprints, and the construction of predictive models. The best results among the machine learning methods for predicting the results of the R. Cattell test were shown by gradient boosting algorithms with the maximum valueof the F1-micro metric of 0.606, which was achieved on the factor “emotional sensitivity” (factor I). The practical significance of the work lies in the development of a tool for automatically predicting the results of the R. Cattell test based on the user's digital footprints. The theoretical significance lies in the development of a method for the automated evaluation of the expression of personality traits of social network users on their digital footprints.

Keywords: 

machine learning, correlation analysis, regression analysis, social media, psychological test, R. Cattell test, social network psychological profile, profiling.

PP. 56-66.

DOI 10.14357/20718632240106 
 
EDN ESXDTN
 
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