How Data Narratives Go Wrong: A Taxonomy of Issues Across the Data Communication Process

2026-07-11

AI Summary

This study addresses the limitation in existing research that often reduces data storytelling issues to isolated chart errors, lacking a systemic understanding of how problems emerge, propagate, and compound throughout the entire data communication pipeline. To bridge this gap, the authors propose the TIC taxonomy—a comprehensive classification framework developed through a literature review and qualitative annotation of 700 real-world cases. The taxonomy spans six dimensions: data, analysis, visualization, text, reasoning, and interpretation, and integrates three key phases—analysis, narrative construction, and audience reception—into a unified diagnostic framework. The project delivers the TIC classification system, an annotated corpus with explicit coding rationales, and an interactive browsing interface, collectively offering structured tools to identify failure modes and enhance the credibility of data narratives.

Abstract

Data narratives increasingly shape public understanding, but their failures are rarely just isolated factual errors or deceptive charts. Instead, they emerge through a broader meaning-making process in which quantitative evidence is transformed into claims, representations, and arguments. While prior work has examined these failures across disparate fields (e.g., statistics, visualization, and fact-checking), the community lacks a holistic lens to explain how these issues arise, propagate, and compound. To address this gap, we introduce TIC, a Taxonomy of Issues in Data Communication, synthesized from prior literature and refined through the qualitative annotation of 700 real-world data narratives from fact-checking sites, research datasets, and controversial media. TIC organizes recurring breakdowns across six dimensions-data, analysis, visual encoding, text, reasoning, and interpretation-and situates them within a framework spanning analysis, narrative construction, and audience reception. Alongside the taxonomy and process framework, we contribute a qualitatively annotated case corpus with coding justifications and an interactive browsing interface. Collectively, these contributions provide a structured lens for diagnosing problematic data narratives and informing future sociotechnical support for trustworthy data communication.

Problem

• data narratives
• data communication
• taxonomy
• meaning-making process
• trustworthy communication

Innovation

• data communication
• taxonomy
• data narratives
• qualitative annotation
• sociotechnical framework