News Production Workflows in Data-driven, Algorithmic Journalism: A Systematic Literature Review

News Production Workflows in Data-driven, Algorithmic Journalism: A Systematic Literature Review

Ausserhofer, J., Gutounig, R., & Oppermann, M. (2015, October). News Production Workflows in Data-driven, Algorithmic Journalism: A Systematic Literature Review. Presented at the Dubrovnik Media Days, Dubrovnik.

In the past decade, the “experimental use of algorithms, data and social science methods” (Gynnild, 2014, p. 715) in journalism has been growing rapidly. What was known as Computer-­assisted reporting and database journalism and what was mainly employed by election analysts, weather reporters, and investigative journalists in the United States (Cox, 2000), is today important for many more practitioners in all kinds of newsrooms and departments all over the world. Data­-driven journalism has become a key theme in journalism education, in the public discourse about journalism, and in journalism research. But in what way have journalistic workflows and routines actually changed due to the broad introduction of data in the newsroom? How has this impacted journalistic norms and ethics, and the skills requirements for journalists?
Based on a systematic review of scholarly papers and industry reports this paper develops a comprehensive conceptual model for newsroom processes in data­driven journalism. It discusses regional differences, the background and epistemology of data journalists, and enabling and hindering factors of data-­driven news projects. In the conclusion, the article identifies gaps in the research landscape that are fruitful for future investigations.

­­--
Cox, M. (2000). The Development of Computer­Assisted Reporting. Presented at the Newspaper Division,
Association for Education in Journalism and Mass Communication, Southeast Colloquium, Chapel Hill,
North Carolina.
Gynnild, A. (2014). Journalism Innovation Leads to Innovation Journalism: The Impact of Computational
Exploration on Changing Mindsets. J ournalism, 1 5( 6), 713–730. http://doi.org/10.1177/1464884913486393

Acknowledgements
The research for this paper is part of the project V isual Analytics in Data-driven Journalism (VALID) t hat is supported by a grant of the A ustrian Ministry for Transport, Innovation and Technology (BMVIT). VALID is financed under the I CT of the Future funding programme of the A ustrian Research Promotion Agency (FFG). Project number: 845598.