A.A. Artamonov1, E.N. Bazhanova2, A.I. Cherkasskiy3, T.V. Korenkova4

1−4 National Research Nuclear University MEPhI (Moscow, Russia)
1 aaartamonov@mephi.ru, 2 enbazhanova@mephi.ru, 3 aicherkasskij@mephi.ru, 4 korenkova.tanya@mail.ru

The article presents an analytical model of a digital information object (AObj) designed for identifying and classifying social media profiles. The model formalizes the description of digital objects through five key components: unique identifier (ID), static (S), dynamic (D), and computed (F) characteristics, along with relationships to other objects (Rel). A comprehensive methodology was developed for model construction, including: ranking characteristics by their significance for specific analytical tasks, converting ranked lists into normalized weighting coefficients (0-1 range), assigning deterministic assessments to characteristic values (numerical, scoring, or thesaurus-based), transforming to relative dimensionless values for integration into a unified criterion, and calculating integral estimates of target computed characteristics as weighted sums of relative values. Specialized methods for analyzing textual and non-textual data within the model were proposed. Specifically, base and specialized thesauri for processing text fields were created, with each thesaurus section assigned weights based on semantic load and usage frequency. For analyzing musical preferences, an audio thesaurus was developed containing compositions thematically related to adolescent protest and self-destruction. Neural network methods were employed for processing avatars and videos, enabling identification of specific visual markers (e.g., presence/absence of human faces, anonymizing elements, symbolism). Experimental results validated the proposed methods for associating accounts with the "school student" social group and detecting bots. The study developed and experimentally verified an analytical digital object model for formalized description of social media profiles. A methodology for calculating integral estimates was proposed, and specialized methods for analyzing textual, audio, and visual data were created. Testing on a sample of 800 profiles confirmed the model's effectiveness for identifying school students, bots, and at-risk users. The research outcomes have practical significance for sociology, psychology, information security, and developing intelligent social data analysis systems.

Artamonov A.A., Bazhanova E.N., Cherkasskiy A.I., Korenkova T.V. Analytical studies of digital objects in a social environment. Highly Available Systems. 2025. V. 21. № 4. P. 42−54. DOI: https://doi.org/10.18127/j20729472-202504-04 (in Russian)

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