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

TL;DR

This paper applies deconstruction and genealogical analysis to explore the unstable meanings and implicit beliefs in visual data journalism, revealing societal influences on perceptions of objectivity and humanism in data storytelling.

Contribution

It introduces a deconstructive and genealogical approach to analyze implicit beliefs in visual data journalism, highlighting societal influences and encouraging diverse visualization research perspectives.

Findings

Identifies opposing implicit beliefs: objectivity vs. subjectivity and humanism vs. mechanism.

Shows these beliefs are shaped by societal forces and historical shifts.

Demonstrates critical theory can reframe success in visual data storytelling.

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. All supplemental materials for this work are available at osf.io/5fr48.