Exploring imagined affordances of automation in news journalism
Stefanie Sirén-Heikel, Leo Leppänen, Carl-Gustav Lindén and Asta Bäck
News automation is an emerging field within journalism, with the potential to transform newswork. Increasing access to data, combined with developing technology, will allow further inquiries into automated journalism. Producing news text using NLG (natural language generation) is currently largely undertaken in specific, predictable news domains, such as sports or finance. This interdisciplinary study investigates how elite media representatives from Finland, Europe and the US imagine the affordances of this emerging technology for their organization. Our analysis shows how the affordances of news automation are imagined as providing efficiency, increasing output and aiding in reallocating resources to pursue quality journalism. The affordances are, however, constrained by such factors as access to structured data, the quality of automation and a lack of relevant skills. In its current form, automated text generation is seen as providing only limited benefits to news organizations that are already imagining further possibilities of automation.