Journalism, Innovation & Datafication
The goal of the module is to advance the students’ understanding of how the journalistic field is affected by datafication, and how journalism, in turn, tries to adapt to change and the different social and technological challenges.
At the end of the course, students are able to:
- understand the nature of (big) data,
- describe how platforms and algorithms are drivers of the datafication of the (media) society,
- explain how data affect the production, distribution, and consumption of news,
- describe the epistemological, economic and ethical issues related to the datafication of journalism,
- discuss the challenges of journalism innovation,
- and, finally, understand how news start-ups become important change-agents in the current news ecosystem.
Journalism is currently undergoing profound transformations. The impact of social media platforms, together with the ubiquity of mobile communication devices that produce a deluge of data, which in turn are analysed by algorithms, shape the current news ecosystem and force journalism to adapt to phenomena like algorithmic recommendation systems, metrics and audience analysis, as well as automated content creation. The course first looks into the nature of (big) data before analysing the interplay between platforms, algorithms and journalism. The central part of the course discusses four different areas in journalism where datafication occurs: observation, production, distribution, and consumption. The course also tackles the question of how the datafication of journalism challenges journalism’s epistemology and ethics, in particular since the algorithmic turn defies professional values such as diversity or transparency. The last part of the course focuses on the issues that the datafication entails for journalism innovation, both within and outside news organizations.
The course adopts a mix of ex-cathedra teaching, groupwork, interactive sessions, and student presentations.
Students’ class attendance is not mandatory, even if strongly encouraged. Teachers will not provide alternative teaching materials for non-attending students.
Evaluation procedures and Grading criteria
- The students will have to carry out an in-class presentation in the last session of the course on a specific topic of their choice.
- There will be a final oral exam on the different topics of the course. The date of the exam is to be determined and will be communicated by the Deanery.
- The final mark for the course will be determined by the in-class presentation (25%) as well as the final oral exam (75%).
- Both the presentation and the exam will be held in English.
- Innovation Research in Organizations, 1990–2018. Journalism Studies, 21(12), 1724-1743.
- Coddington, M. (2015). Clarifying Journalism’s Quantitative Turn. Digital Journalism, 3(3), 331-348.
- Kitchin, R. (2014). The Data Revolution: Big Data, Open Data, Data Infrastructures & Their Consequences. London: Sage.
- Lewis, S. C. & Westlund, O. (2015). Big Data and Journalism. Epistemology, expertise, economics, and ethics. Digital Journalism, 3(3), 447-466.
- Loosen, W. (2018). Four forms of datafied journalism. Journalism’s response to the datafication of society. Communicative Figurations working paper no. 18. Bremen: University of Bremen.
- Porlezza, C. (2018). Deconstructing data-driven journalism. Reflexivity between the datafied society and the datafication of news work. Problemi dell’Informazione, Vol. 3, 369-392.
- Radcliffe, D., & Lewis, S. C. (2021). The Datafication of Journalism: Strategies for Data-Driven Storytelling and Industry-Academy Collaboration. In: L. Bounegru & J. Gray (eds.), The Data Journalism Handbook (pp. 314-330). Amsterdam: Amsterdam University Press.
This is only a small selection of the readings. All the mandatory reading materials will be made available on the platform ICorsi.
Master of Science in Communication in Media Management, Corso di base, 1° anno