Data Visualizations in Explanatory Journalism
For the casual news reader, numerical data is often viewed as a source of impartial, scientific evidence. Because of these associations with objectivity, including data visualizations like charts and graphs in a news article might lend the piece a sense of credibility—regardless of whether the information depicted is actually relevant to the text.
This project investigates how quantitative data is portrayed in The Conversation by examining data visualizations included in The Conversation Canada articles about the COVID-19 pandemic. Using a mixed-methods approach, Dr. Mudry’s team is taking a closer look at the kinds of data visualizations that are used in The Conversation articles, and to what extent the data represented in these images actually works to support the claims being made by the author.
In addition to investigating the relevance of data visualizations to The Conversation articles, this project also considers the sources and subjects of the data being depicted. The COVID-19 pandemic has continuing, wide-reaching effects on a global scale. During this worldwide event, which countries’ data is most often cited? Whose management of and research into the virus are most often deemed relevant on the world stage? Which countries’ experiences are left out of this conversation, and how might this reflect and uphold biases toward particular countries or regions? These potential biases are further interrogated by examining the tone and language authors use to refer to different countries’ data, with a focus on whether there is any evidence of biased language toward specific countries or regions, and how this might perpetuate particular narratives or biases in both the academy and popular discourse.
Examining the role of visualizations on The Conversation will contribute to a broader understanding of how quantitative data is used in explanatory writing. Does including numerical data in news articles help or hinder readers’ understanding of the topic at hand? This question becomes especially pressing when, as preliminary results suggest, the data visualizations included in an article sometimes do little to validate the arguments being presented in the text.
The Explanatory Journalism Project is supported in part by funding from the Social Sciences and Humanities Research Council.