The production of data journalism in the era of AI: the transformation of political news and visualization strategies in China and Russia

The subject of the research is the production process of political data journalism and related visualization strategies in two institutionally different media environments—China and Russia—in the context of the active implementation of artificial intelligence tools and big data analytics. The focus of the research includes editorial practices, technological and organizational drivers, as well as specific visualization solutions in the publications of news organizations (the specialized sections Data News of Xinhua and Infographics of TASS). The analysis focuses on data selection and verification processes, the architecture of hybrid editorial teams, algorithmic content editing, the transparency of methodology, and the communicative function of visualizations in the representation of political events. The study covers the period from 2022 to 2024 and is based on a comparative content and visual-analytical analysis of relevant publications; attention is paid to institutional differences that affect the quality and interpretation of political data products. The methodological foundation is an interdisciplinary approach combining theories of media communication, political linguistics, and data science: methods include qualitative content analysis of publications, comparative analysis, case studies, and a review of professional literature. The scientific novelty of the study lies in the integration of original concepts from both domestic and foreign researchers with empirical analysis of relevant data sections of news organizations to formulate generalized conclusions about the transformation of political news production under the influence of AI. For the first time, the comparison includes: (a) institutional access to administrative data and state infrastructure (PRC) with practices of independent editorial openness and reproducibility (Western media and certain Russian projects); (b) the level of methodological transparency and the degree of interactivity of visualizations; (c) editorial architectures (hybrid teams of journalists, analysts, and developers). Key findings: the institutional context determines not only technical capabilities but also the frameworks for editorial autonomy and critical interpretation; access to government databases accelerates the creation of detailed visualizations but does not guarantee methodological transparency; independent projects demonstrate best practices in verification and openness but face resource constraints. The practical contribution includes recommendations for implementing editorial protocols for methodological transparency, mandatory documentation of data sources, and training journalists in critical skills for working with algorithms and visualizations.