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The Impact of Generative Neural Networks on Mass Consciousness: A Comparative Analysis of Western, Russian, and Chinese Neural Networks

https://doi.org/10.55959/MSU2070-1381-116-2026-124-140

Abstract

The article presents a comparative analysis of value orientations across six generative neural networks of different national origins: American ChatGPT and Claude, Chinese DeepSeek, French Mistral AI, and Russian YandexGPT and GigaChat. The rapid penetration of such systems into everyday information environments raises the question of what ideological attitudes they transmit to their hundreds of millions of users. The study aims to identify and systematize these attitudes and to describe the mechanisms through which they influence the components of political consciousness. The methodological framework combines critical discourse analysis, the concept of algorithmic bias, and frame analysis. A structured questionnaire comprising seven blocks was developed to test the models, covering political values, beliefs, knowledge, interpretations of events, emotional reactions, needs, and evaluations. The study was conducted from June to August 2025; all prompts were submitted in Russian in a standardized form. The findings show that American and European models consistently reproduce liberal-democratic attitudes, while DeepSeek combines relative openness with rigid blocking of topics sensitive to the PRC. Russian models fail to form an independent value framework: on politically neutral questions their responses effectively replicate the Western frame, whereas exposure to contentious issues triggers an avoidance strategy. The frame analysis identified four stable discursive frames: liberal-universalist, sovereign-pragmatic, declared neutrality, and selective blocking. Seven mechanisms of influence on users’ political consciousness were identified: normalization, framing, selective knowledge retrieval, attribution of motives, emotional labeling, agenda-setting, and asymmetric criticism. The study concludes that the declared technological sovereignty of domestic models is not substantiated by any meaningful value alternative and effectively cedes the information space to Western competitors.

About the Authors

Yu. Yu. Petrunin
Lomonosov Moscow State University
Russian Federation

Yuriy Y. Petrunin, School of Public Administration, DSc (Philosophy), Professor

Moscow



O. A. Yamanova
Financial University under the Government of the Russian Federation
Russian Federation

Olga A. Yamanova, Postgraduate student, Intern Researcher of the Center for Political Research

Moscow



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For citations:


Petrunin Yu.Yu., Yamanova O.A. The Impact of Generative Neural Networks on Mass Consciousness: A Comparative Analysis of Western, Russian, and Chinese Neural Networks. Public Administration. E-journal (Russia). 2026;(116):124-140. (In Russ.) https://doi.org/10.55959/MSU2070-1381-116-2026-124-140

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ISSN 2070-1381 (Online)