Preview

Communicology

Advanced search

Neurogenerative Technologies for Video Content Production: Media Political Discourse

https://doi.org/10.21453/2311-3065-2026-14-1-86-96

Abstract

This article examines the specific application of neurogenerative technologies in the production of visual content serving as an element of political media communication. It is noted that the emergence of synthetic technologies opens new prospects for the creation of video content in demand in the media practices of modern Russian television companies. The authors find that generative media content is actively used as a tool for visualizing complex  political processes, facilitating their perception and offering original ways of understanding the political agenda. Thus, synthetic media content acts not as a replacement for documentary images, but as an expanded media language, complementing traditional journalistic genres and approaches to understanding reality. The empirical basis for the study was video footage from the RT television channel, one of the key global media holdings mastering AI technologies and integrating neurogenerative content into political media communications. In the course of the study, the authors confirm the hypothesis that RT uses synthetic media content not so much to imitate documentary authenticity, but rather to reinforce key points, increase emotional impact, and structure the political narrative. Synthetic elements serve as tools for visualizing abstract concepts, modeling hypothetical situations, and creating emotional resonance, while the channel strives for transparency by labeling the synthetic nature of the media content it posts.

About the Authors

A. L. Kodanina
Nizhny Novgorod State University named after N.I. Lobachevsky
Russian Federation

Anna Lvovna Kodanina – Candidate of Political Sciences, Associate Professor, Associate Professor of the Department of Journalism 

603000, Nizhny Novgorod, Bolshaya Pokrovskaya Street, 37



E. D. Mosina
Nizhny Novgorod State University named after N.I. Lobachevsky
Russian Federation

Ekaterina Dmitrievna Mosina – Master’s student of the Department of Journalism 

603000, Nizhny Novgorod, Bolshaya Pokrovskaya Street, 37



T. E. Novikova
Nizhny Novgorod State University named after N.I. Lobachevsky
Russian Federation

Tatyana Evgenievna Novikova – Candidate of Philosophical Sciences, Associate Professor of the Department of Journalism

603000, Nizhny Novgorod, Bolshaya Pokrovskaya Street, 37



References

1. Arsentyeva A.D., Nikolaeva M.O. (2022). The Role of Artificial Intelligence Algorithms in the Distribution and Tracking of Destructive Content // MEDIA Education: Digital Environment in the Context of Forced Metamorphosis. Collection of Materials of the VII International Scientific and Practical Conference. Chelyabinsk: ChSU, pp. 556-671 (in Rus.).

2. Beinenson V.A. (2023). Application of generative neural networks in journalism: problems and prospects // Dynamics of media systems. Vol. 3. No. 1. P. 352-359 (in Rus.).

3. Davydov S.G., Zamkov A.V., Krasheninnikova M.A., Lukina M.M. (2023). Use of artificial intelligence technologies in Russian media and journalism // Bulletin of Moscow University. Series 10. Journalism. No. 5. P. 84-116 (in Rus.).

4. Dugin E.Ya. (2024). Transformation of media communication under the influence of digital technologies: theoretical and methodological aspect. Bulletin of Moscow University. Series 10: Journalism. No. 5. P. 140-151(in Rus.).

5. Fedorov V.I. (2025)/ Integration of AI into media platforms: technological and social aspects // Information Society. No. 1, pp. 60-67 (in Rus.).

6. Galyashina E.I., Nikishin V.D., Bogatyrev K.M., Pfeifer E.G. (2023) Fake news as a means of information warfare in Internet media: a scientific and practical guide. Moscow: Blok-Print (in Rus.).

7. Gorbunova E.A. (2023). Artificial intelligence in the media space: technical and legal aspects // Journal of Communication Studies. No. 2, pp. 45-52 (in Rus.).

8. Lazutkina E.V. (2024). Specifics of organizing training in the field of working with neural networks in media communications // Actual problems of regional journalism. Rostov-on-Don. pp. 197-205 (in Rus.).

9. Lugovaya E.D. (2025). Using neural networks in journalism to generate data content // Young scientist. No. 11 (562), pp. 4-6 (In Rus.).

10. Marconi F. (2020). Newsmakers: Artificial Intelligence and the Future of Journalism. New York: Columbia University Press. 202 p.

11. Morhat P.M. (2017). Artificial intelligence: a legal view: scientific monograph. M.: Buki Vedi (in Rus.).

12. O'Neil C. (2016). Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. New York: Crown Publishers. 259 p.

13. Paradigms, and Themes//Communicology. Vol. 13. No. 4, pp. 13-21 (in Rus.). Sharkov F.I., Andrianova T.V. (2025) .Media Inclusion: Genesis,

14. Sharkov F.I., Potapchuk V.A., Golushko I.I. (2025). Media Industry Innovations: New Media and Artificial Intelligence // Communicology. Vol. 13. No. 1, pp. 13-24 (in Rus.).

15. Zubanova L.B. (2022) Artificial Intelligence in Ethical Conditions: Unintentional Cruelty and Constructed Bias // SMI Education: Digital Environment in Conditions of Forced Metamorphosis. Collection of materials from the VII International scientific and practical conference. Chelyabinsk: ChSU, pp. 577-581 (in Rus.).


Review

For citations:


Kodanina A.L., Mosina E.D., Novikova T.E. Neurogenerative Technologies for Video Content Production: Media Political Discourse. Communicology. 2026;14(1):86-96. (In Russ.) https://doi.org/10.21453/2311-3065-2026-14-1-86-96

Views: 69

JATS XML


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.


ISSN 2311-3065 (Print)
ISSN 2311-3332 (Online)