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Deconstructing Implicit Beliefs in Visual Data Journalism: Unstable Meanings Behind Data as Truth & Design for
IEEE Transactions on Visualization and Computer Graphics
|November 19, 2025
Summary
This study deconstructs visual data journalism, revealing inherent tensions between objectivity/subjectivity and humanism/mechanism. These beliefs are shaped by societal forces, not isolated, impacting data storytelling success.
Area of Science:
- Qualitative research methodology
- Critical theory application in data visualization
- Sociotechnical analysis of information design
Background:
- Visual data journalism integrates journalistic practices with data visualization.
- Existing research often overlooks the implicit, often contradictory, beliefs underpinning the field.
- Understanding these beliefs is crucial for advancing data storytelling and visualization research.
Purpose of the Study:
- To deconstruct implicit beliefs within visual data journalism using critical theory.
- To explore the instability of meaning in language used in visual data journalism.
- To reframe 'success' in visual data storytelling by examining underlying assumptions.
Main Methods:
- Qualitative interview study with 17 international visual data journalists.
- Application of deconstruction approach from literary critique to analyze interview data.
- Genealogical analysis to contextualize identified beliefs within historical and societal frameworks.
Main Results:
- Identified two sets of opposing implicit beliefs: objectivity versus subjectivity, and humanism versus mechanism.
- Demonstrated that these beliefs are influenced by external societal forces and historical paradigm shifts.
- Challenged the notion of self-enclosed beliefs within visual data journalism.
Conclusions:
- Critical theories like deconstruction and genealogy offer valuable frameworks for analyzing visual data journalism.
- Reframing 'success' in visual data storytelling requires acknowledging and understanding these implicit beliefs.
- Diversifying visualization research outcomes necessitates examining the sociotechnical issues surrounding datafication and data visualization.
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