Machinic Journalism? Automation of Work and Data Analytics in Professional News Production

Abstract

Introduction: Machine learning systems have a dual relationship with human labor: on the one hand, they need to exploit it to function effectively; on the other, they regulate it, as they are used for surveillance, monitoring working hours, and productivity. In this article, we will address the interaction of these systems with journalistic work in newsrooms that produce written information. Objectives: To analyze the implications that automation and data analytics have on: 1) The work process. 2) Journalistic products. 3) News agendas and newsworthiness criteria. Methodology: As this is an academic essay, this approach is based on a critical review of the literature combined with an analysis of empirical examples, drawing on documentary review and personal communications with journalists. Results: The review reveals changes in production routines and journalists' productivity expectations, the emergence of non-journalistic profiles that are integrated into the newsroom work process, transformations in writing formats and styles, and the impact on the ways in which journalistic information is selected, prioritized, and thematized. Conclusions: The discussion offered in the essay takes a critical look at the consequences that the changes in the three aspects analyzed have on the organization of journalistic work and its agendas, which are affected by opaque criteria for those who produce and consume the information. In addition, it raises questions about the creative dimension of journalistic work.