Development of an Algorithm of Identification of Risk Factors for Social Media User Safety Based on Content Analysis and Psychological Characteristics of Content Consumers

Project purpose

The project is aimed to solve the problems of creation of a complex model of identification and forecasting of risk factors for social medial user safety based on analysis of social media content and individual psychological characteristics of users (anxiety, aggression, character typology).


  • Identification and classification of forms of unsafe social media content in the Russian Internet segment.
  • Development of social media monitoring tools based on digital footprints, user behavioral and psychological data with determination of the main risk factors for safety and identification of users within the risk groups.
  • Development of a web application for self-diagnostics of online security risks, as well as recommendations for working with high-risk groups among children, teenagers and youth based on actual data on information trends in social media.

Results for June 2021

  1. The accuracy of the model of forecasting of safety risk factors is verified by user data in VKontakte.

  2. Adjusted protocol of psycho-diagnostic research of psychological, individual topological features of social media users.

  3. Adjusted (updated) web application for psycho-diagnostics of psychological, individual topological features of social media users.

  4. The data base of psycho-diagnostic research of psychological, individual topological features of high school seniors and university students. The survey sample included 7,505 university students and 1,344 high school students (14~18 y.o.).

  5. A web application is developed for monitoring the safety risk factors among students by means of user data in VKontakte.

  6. Methodological recommendations are developed for monitoring of safety risk factors (for a student, group of students, establishment, region).

  7. 2 publications in editions indexed by Web of Science or Scopus, 2 publications in editions under the Russian Science Citation Index and the State Commission of Academic Degrees and Titles.

  8. Prepared IP results (data bases, web applications for psychodiagnostics of VKontakte users and collection of open data from user accounts).

  9. Adjusted methodology of initial identification of destructive (deviant) communities.

  10. Description of the structure and communication strategies of destructive (deviant) communities.

  11. The model of spread of destructive content in VKontakte.

Project team

  • Valeria Matsuta — PhD Psychology, Assistant Professor of the Chair of Organizational Psychology, Faculty of Psychology of TSU
  • Artyom Feshchenko — Research Team Leader, Chief of Laboratory of Computer-Based Training Aids of TSU Institute of Distance Education
  • Vyacheslav Goyko — Director of Applied Data Mining Center of TSU
Project details

We are looking for cooperation with educational organizations. Please write to coordinator A.V. Feshchenko fav@ido.tsu.ru to join the use of the student diagnostics and monitoring system.