Data journalism: the problem of definition and approaches to interpretation
DOI:
https://doi.org/10.47475/2070-0695-2026-59-1-150-159Keywords:
data journalism, data visualization, quantitative journalism, computer-assisted reportingAbstract
The main goal of this study is to propose a comprehensive multi-level definitional model of "data journalism." To achieve this, the following objectives are set: to systematize existing definitions; to identify key interpretation criteria. The study contributes to the field by classifying conceptual approaches, addressing definitional fragmentation, and highlighting research gaps (e.g., cross-cultural studies, AI impact).
Theoretical framework builds on a meta-analysis of conflicting and consensual criteria (open data, visualization, computational tools, etc.) from academic literature. Methodology combines qualitative content analysis to determine the frequency of key features in the selected works, comparative analysis of definitions, functional analysis to formulate criteria, descriptive analysis to summarize statistical data, and analytical modeling to construct a three-level model. Classification of definitions is performed by levels. Research material comprises 58 scientific publications (2010–2024) from Scopus, Web of Science, and Russian peer-reviewed journals, selected based on an exhaustive keyword search for "data journalism", "журналистика данных", "дата-журналистика" with subsequent filtering based on explicit definitions, empirical/theoretical methods, and peer-reviewed status.
Results reveal five core criteria: open data (89 % of sources), computational tools (76 %), visualization (68 %), interactivity (52 %), and scientific methods (34 %). A three-level model is proposed: (1) technical (data collection, cleaning, analysis), (2) creative (interpretation and narrative design), (3) communicative (visualization and audience interaction).
Conclusions emphasize the dual nature of data journalism, bridging technological innovation with traditional journalistic values. Limitations include geographical bias (72 % Western cases) and methodological reliance on theoretical works. Future research should address cross-cultural practices, empirical validation of the model, and ethical implications of AI integration.
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