'DATA, CAMERA, ACTION: HOW ALGORITHMS ARE SHAKING UP EUROPEAN SCREEN PRODUCTION'

  • Nina Vindum Rasmussen King's College London, United Kingdom
Keywords: Algorithms, data-driven creativity, screen production, labour

Abstract

Films and television series you watch online are watching you back. Algorithms and data analytics are making deeper inroads into film and television production in Europe: Belgian company ScriptBook offers data-driven script analysis and automated story generation, which the company sees as co-authorship between humans and machines. At the same time, data-driven streamers like Netflix and Amazon are investing heavily in local-language content. This paper examines how these developments affect creative labour in the European screen industry. More specifically, it zooms in on the development stage and the experiences of screenwriters, directors, and producers. What do audience data and algorithmic tools add to the creative process? What are the possibilities and limitations? Questions like these call for a robust theoretical and methodological toolbox, which synthesises concepts and methods from media industry studies, critical algorithm studies, and critical data studies. This research project explores the influence of algorithms and data analytics on both a macro-level (changing industry structures) and micro-level (creative practices of screenwriters and producers). The analysis is based on data from three registers: semi-structured interviews with screen workers, ethnographic field observation, and industry trade publications. These empirical data have been gathered online and offline in several European countries. In sum, this paper presents some preliminary observations of European screen production in an algorithmic culture - and how it is perceived by the people who produce the stories that land on our screens.

Published
2020-10-05
How to Cite
Rasmussen, N. V. (2020). ’DATA, CAMERA, ACTION: HOW ALGORITHMS ARE SHAKING UP EUROPEAN SCREEN PRODUCTION’. AoIR Selected Papers of Internet Research, 2020. https://doi.org/10.5210/spir.v2020i0.11311
Section
Papers R