Deconstructing Implicit Beliefs in Visual Data Journalism: Unstable Meanings Behind Data as Truth & Design for Insight

2025-07-16

AI Summary

This study interrogates the instability of implicit beliefs—such as “data as truth” and “design as insight”—in visual data journalism, examining how they are shaped by sociopolitical forces and paradigmatic shifts. Drawing on qualitative interviews with 17 global visual data journalists and employing deconstructive and genealogical methodologies, it uncovers two foundational antinomies embedded in data visualization practices: “objectivity/subjectivity” and “humanism/mechanism,” tracing their sociohistorical origins. Its key contribution lies in challenging the taken-for-granted objectivity of data, integrating literary-critical theory into visualization studies, and reconceptualizing success criteria for visual data narratives. Beyond exposing discursive ambiguity and latent ideology, the work advances a value-pluralist epistemology for visualization knowledge production—shifting focus from technical determinism toward ethical, political, and aesthetic pluralism—and thereby extends the critical and reflexive scope of data journalism research.

Technology Category

• Data Mining & Knowledge Management: Data Visualization & Summarization
• Computer Vision: Bias, Fairness & Privacy
• Application Domains: Humanities & Computational Social Science

Application Category

• Web Mining and Content Analysis: Web data visualization
• Security and Privacy: Data transparency and provenance
• Semantics and Knowledge: Provenance, trust, security and privacy, and ethical issues in managing semantic data

Abstract

We conduct a deconstructive reading of a qualitative interview study with 17 visual data journalists from newsrooms across the globe. We borrow a deconstruction approach from literary critique to explore the instability of meaning in language and reveal implicit beliefs in words and ideas. Through our analysis we surface two sets of opposing implicit beliefs in visual data journalism: objectivity/subjectivity and humanism/mechanism. We contextualize these beliefs through a genealogical analysis, which brings deconstruction theory into practice by providing a historic backdrop for these opposing perspectives. Our analysis shows that these beliefs held within visual data journalism are not self-enclosed but rather a product of external societal forces and paradigm shifts over time. Through this work, we demonstrate how thinking with critical theories such as deconstruction and genealogy can reframe "success" in visual data storytelling and diversify visualization research outcomes. These efforts push the ways in which we as researchers produce domain knowledge to examine the sociotechnical issues of today's values towards datafication and data visualization.

Problem

• Examines implicit beliefs in visual data journalism
• Analyzes objectivity/subjectivity tensions in data storytelling
• Explores societal influences on data visualization paradigms

Innovation

• Deconstruction approach from literary critique
• Genealogical analysis for historic context
• Critical theories reframe visual storytelling success