O. N. Bodin1, V.M. Zhigachev2, U.N. Balaba3, I.V. Golubkova4

1–4 Penza State Technological University (Penza, Russia)
1 bodin_o@inbox.ru, 2 zhigachevvm@mail.ru, 3 balabaul2017@gmail.com, 4 ilonna1990@mail.ru

Social networks and messengers are becoming one of the main intermediaries in the communication of participants in the media space. Simplification techniques, such as the gamification of communication between people, have significantly expanded the possibilities for manipulation. Psychological behavior developed by individuals and groups of people in the digital environment works differently: it can cause severe emotional discomfort or chronic frustration. Significant differences between the level of needs and expectations of personal achievements are a significant prerequisite for the emergence of conflict. A person who is unable to be satisfied with their achievements has excessive consumer expectations. In psychology, influence is the purposeful transmission of information from one participant to another. Media influence can be remote, one-sided, or interactive.

Goal – the purpose of this article is to analyze the digital footprint of a social network user in order to identify dangerous individuals. By analyzing the digital footprint of a social network user, it is possible to assess the deformation of their personality.

The application of joint analysis of multimodal data of a social network user will improve the detection of emotional state, increase the accuracy of determining emotions, expand the range of detection of complex states, aggression – fear, disgust and annoyance. Due to the pre-processing of media content, accuracy improves, the volume of analyzed media content is reduced and the speed of processing is significantly increased.

As a result of the proposed algorithm for analyzing a social media user's digital footprint, a psychoemotional chart is created that indicates the manifestations of extreme psychological states and the factors that influence them.

Bodin O.N., Zhigachev V.M., Balaba Yu.N., Golubkova I.V. Algorithm for analyzing the digital footprint of a social network user. Bio­medicine Radioengineering. 2025. V. 28. № 5. P. 129–136. DOI: https:// doi.org/10.18127/j15604136-202505-26 (In Russian)

1. V Rossii zapuskayut nacproekt po cifrovoj transformacii gosudarstva https://rg.ru/2024/05/21/kakie-nashi-kody.html (In Russian).
2. Olinder N., Tsvetkov A., Fedyakin K., Zaburdaeva K. Using digital footprints in social research: an interdisciplinary approach. WISDOM. 2020. № 3. P. 124–135.
3. Navarro Dzh. Opasnye lichnosti. Kak ih vychislit' i ne dat' manipulirovat' soboj: Per. s angl. M.: Eksmo. 2024. 352 s. (In Russian).
4. Catch Me If You Can: Big Data and Crime Prevention. URL: https://www.alleywatch.com/2014/08/catch-me-if-you-can-big-data-and-crimeprevention/].
5. Metody data mining: obzor i klassifikaciya. URL: https://hsbi.hse.ru/articles/metody-data-mining-obzor-i-klassifikatsiya/ (In Russian).
6. Chereshnev E. Forma zhizni №4. Kak ostat'sya chelovekom v epohu rascveta iskusstvennogo intellekta. M.: Al'pina Pablisher. 2024. 480 s. (In Russian).
7. Patent № RU2586854C1 (RF). Sposob predostavleniya dannyh, otnosyashchihsya k pacientam medicinskogo uchrezhdeniya / O.N. Bo­din i dr. 2016 (In Russian).
8. Prohorov A., Lysachev M. Cifrovoj dvojnik. Analiz, trendy, mirovoj opyt. Nauch. red. prof. A. Borovkov. Izd. 1-e, ispr. i dop. M.: OOO «Al'yansPrint». 2020. 401 s. (In Russian)