What Do We Mean When We Talk About Data Storytelling?

2025-10-06

AI Summary

The conceptual boundaries of data storytelling remain ambiguous, impeding scholarly communication, hindering theoretical development, and constraining practical applications. To address this, we systematically analyzed 96 interdisciplinary studies that explicitly define “data storytelling,” employing qualitative coding to identify five original definitional paradigms—corresponding respectively to content composition, narrative purpose, technical implementation, cognitive mechanisms, and socio-functional roles. This yields the first multidimensional interpretive spectrum of data storytelling. The framework clarifies both the diversity of conceptual usage and its underlying logical structure, transcending reductive perspectives centered solely on technology or rhetoric. By integrating inclusivity with analytical precision, it establishes a robust theoretical foundation for the field, facilitating subsequent research positioning, methodological selection, and tool design.

Technology Category

• Data Mining & Knowledge Management: Data Visualization & Summarization
• Humans and AI: Game Design — Procedural Content Generation & Storytelling
• Application Domains: Humanities & Computational Social Science

Application Category

• Web Mining and Content Analysis: Web data visualization
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Responsible Web: Ethical and legal aspects of web-scale data analysis, uses and collection practices

Abstract

We have witnessed rapid growth in data storytelling research. Scholars from multiple disciplines have contributed new theories and techniques surrounding data storytelling. However, with this prolific development, a fuzzy boundary of data storytelling comes. We argue that understanding how "data storytelling" has been defined and interpreted by academia is crucial for facilitating communication between researchers, encouraging the consistent use of concepts and measures, assisting newcomers in approaching and positioning their research in this area, and enabling the effective application of relevant techniques and tools. Thus, it is necessary to systematically reflect on "what is data storytelling" and promote a more thorough understanding of this concept. Specifically, we investigated how existing research has conceptualized "data storytelling." As a result, we identified 96 publications that provide explicit definitions. By coding these definitions in-depth, we identified five paradigms of defining data storytelling, as well as a broad spectrum of interpretations regarding the content, objectives, and techniques of data storytelling. Finally, we concluded with implications for future research, aiming to foster nuanced communication about "data storytelling," suggest research opportunities, and establish a more inclusive theoretical foundation for this research direction.

Problem

• Defining data storytelling's conceptual boundaries across disciplines
• Identifying five paradigms in existing data storytelling definitions
• Establishing inclusive theoretical foundation for future research

Innovation

• Systematically reviewed existing data storytelling definitions
• Identified five paradigms through in-depth coding analysis
• Established inclusive theoretical foundation for future research