Data Journalism and the Challenge of Shoe-Leather Epistemologies

After prediction-defying elections in 2015 and 2016 in the United Kingdom and the United States rattled journalists, some publicly blamed data journalism—an indictment that raised questions about journalistic discourse surrounding the polysemic phrase and still-developing field. A content analysis (n = 612) of all published news stories over two years in four international news databases revealed the potential of journalists to embrace data as an empirical tool, for the phrase was most frequently associated with numerical analysis. However, the next most-frequent definitional category was electoral prediction, which was statistically more likely in the nations where data journalism is most often practiced, the U.K. and U.S. Correlating data journalism with electoral prediction also was statistically more likely in the month immediately after the surprising elections. Further, it was more likely to stimulate pushback from practitioners who saw data as epistemologically less reliable than “shoe-leather” approaches marked by observation and interviews with a few people perceived to be voter archetypes. The results reveal more than journalistic ignorance about polling techniques. They reveal an element of news culture that privileges traditional methods and dismisses unfamiliar evidentiary tools, which risks skewing audience understanding of data journalism.