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Autor/inn/enGafarov, Fail M.; Nikolaev, Konstantin S.; Ustin, Pavel N.; Berdnikov, Andrey A.; Zakharova, Valeria L.; Reznichenko, Sergey A.
TitelA Complex Neural Network Model for Predicting a Personal Success Based on Their Activity in Social Networks
QuelleIn: EURASIA Journal of Mathematics, Science and Technology Education, 17 (2021) 10, (9 Seiten)
PDF als Volltext kostenfreie Datei    Verfügbarkeit 
Spracheenglisch
Dokumenttypgedruckt; online; Zeitschriftenaufsatz
ISSN1305-8223
SchlagwörterSocial Media; Social Networks; Behavior Patterns; Prediction; Cognitive Processes; Audio Equipment; Video Technology; Accuracy; Foreign Countries; Artificial Intelligence; Comparative Analysis; Correlation; Psychological Patterns; Individual Characteristics; Computer Mediated Communication; Data Analysis; Profiles; Computer Software; Success; Russia
AbstractThe 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 (kmeans). 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. (As Provided).
AnmerkungenModestum. No: 1 Windrush Road, Hilton Derbyshire, DE65 5LB, UK. e-mail: ejmste@ejmste.com; Web site: https://www.ejmste.com/
Erfasst vonERIC (Education Resources Information Center), Washington, DC
Update2022/1/01
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