THE STRUCTURE OF MEDIA COMMUNICATIONS OF LOCAL GOVERNMENT BODIES: EXPERIENCE OF COMPREHENSIVE ANALYSIS
In the context of new requirements for the organization of media content on the o cial resources of local authorities, the emergence of new publication formats is observed, which creates the need to study them and search for optimal models of media messages. The main goal of the study is to analyze which structural elements of messages are most optimal under the conditions of these requirements. This article discusses the synthesis of content research methods - content analysis and structural compositional analysis, which provide a deeper understanding of how various factors a ect the perception of information by users. The author o ers his own approach to conducting a content analysis of the o cial page of the local authorities on the VKontakte social network using the example of the page of the administration of the Korochansky district of the Belgorod region. Using the transformed formula of the engagement coe cient by views, popular and unpopular publications were selected for the study period. Structural elements of 160 publications were analyzed according to 14 criteria, including the type and number of media files, adherence to corporate identity, text volume, the presence of persons, emoji, links, hashtags and other factors. In the work, the author’s formula was proposed for calculating the optimal value of the structural elements of publications, presented in the format of a table. The analysis revealed key characteristics of structural elements that a ect the popularity of publications. Conclusions were also drawn on the practical application of the proposed approach, its strengths and weaknesses, as well as the prospects for further development. The results of the study can be useful for specialists of the press services of local authorities seeking to optimize the organization of media content.
Institutions
- Belgorod National Research University (RU)
Publication Details
- Journal
- Sign problematic field in mediaeducation
- Published
- 2025-09-15
- DOI
- https://doi.org/10.47475/2070-0695-2025-57-3-20-29
- Primary Topic
- Brain Tumor Detection and Classification
- Type
- article
- Field-Weighted Citation Impact
- 0.00