Data-Driven Revelation?

As an increasing number of reporters see databases and algorithms as appropriate means of doing investigation, journalism has been challenged in recent years by the following question: to what extent would the processing of huge datasets allow journalists to produce new types of revelations that rely less on normative assumptions? Drawing on the analysis of a particular investigation by the San Francisco-based Center for Investigative Reporting, this article points out the existence of epistemological tensions in the making of journalistic revelations that involve the processing of vast amounts of data. First, I show that the design of data-processing artifacts can match the traditional epistemology of journalistic investigation, but only with great efforts and resources from the organization. Second, I point out that the use of these artifacts by journalists follows two opposite paths to produce the revelation: a “hypothesis-driven” path and a “data-driven” path. Such findings contribute to a better understanding of how news organizations produce justified beliefs, as data-processing artifacts become major components of the newsroom’s environment.