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12:00 PM  February 18, 2020

TSU application allow to find aggressive teenagers in social networks

© РИА Томск. Павел СтефанскийTSU application allow to find aggressive teenagers in social networks

TOMSK, Feb 18 – RIA Tomsk. Tomsk State University (TSU) scientists will develop a web application that can determine dangerous psychological conditions of children and adolescents by their digital footprint on the VKontakte social network by 2022; the project was supported by Russian Science Foundation grant, research team member Artyom Feschenko told RIA Tomsk on Monday.

He specified that the project is called "Development of an algorithm for identifying security risk factors for users of social networks based on analysis of content and psychological characteristics of its consumers".

Follow the digital footprint

"According to VCIOM (Russian Public Opinion Research Center), 90% of Russian children and adolescents use the Internet and social networks. Social networks today - are not only a means of communication, but also a means of realizing aggression. The purpose of our study - is to find the relationship between psychological factors in the manifestation of deviant behavior of adolescents in their daily lives and their behavior in digital environment", - comments Feschenko.

Scientists of various profiles participate in the new project of TSU - employees of the faculty of psychology, the laboratory of science for big data and problems of society, the Institute of distance education. Together they combine the methods of psychology, computer linguistics, computer science, social network and statistical analysis.

The hypothesis of scientists is as follows: the psychological state of a person (in this case, a schoolstudent) is reflected in his digital footprints on social networks. These are, first of all, VKontakte subscriptions for thematic communities, likes, published texts and images. And each such condition corresponds to certain combinations of the digital trace.

“For example, a person who is prone to aggressive behavior in everyday life, on social networks may be more interested in the subject of weapons, power combat or various forms of open violence more actively than other subject, at the same time he will have relatively few users in the list of friends, little familiar with each other", - says the scientist.

Scientists will test this hypothesis in two ways. They will use a diagnostic tool to measure a person’s psychological state, and then compare digital footprint with opposite results, for example, a high level of anxiety with a low level.

“So we are looking for differences in digital footprints. After identifying the characteristics of digital behavior in people with high levels of anxiety, we will be able to create tools that are highly likely to predict the same state on the profile in a social network for users who have not undergone special diagnostics”, - explains the interlocutor of the agency.

From observation to web application

The second way to test the hypothesis - is to monitor the content and users of those communities on social networks that can be potentially dangerous. These are groups about alcohol, depression, weapons, the aesthetics of death, hatred and so on.

“It’s important here to explore the mechanism by which dangerous content is distributed and the motivation of the people who generate it”, - says Feschenko.

If scientists confirm their hypothesis in the first stages of the study, they will proceed to the development of a web application for self-diagnosis of psychological characteristics and self-regulation of “unsafe” human conditions. Also, the web application will be useful to school psychologists. With it, they will be able to monitor the safety of students according to their digital footprints.

The project will be completed in 2022. Its financial support from the RSF for each subsequent year depends on the interim results of the team. According to preliminary plans, each year the Russian Science Foundation will provide researchers with 5 million rubles each year.

Наверх
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