A systematic review of automated journalism scholarship: guidelines and suggestions for future research

Background: The use of advanced algorithmic techniques is increasingly changing the nature of work for highly trained professionals. In the media industry, one of the technical advancements that often comes under the spotlight is automated journalism, a solution generally understood as the auto generation of journalistic stories through software and algorithms, without any human input except for the initial programming. Methods: In order to conduct a systematic review of existing empirical research on automated journalism, I analysed a range of variables that can account for the semantical, chronological and geographical features of a selection of academic articles as well as their research methods, theoretical backgrounds and fields of inquiry. I then engaged with and critically assessed the meta-data that I obtained to provide researchers with a good understanding of the main debates dominating the field. Results: My findings suggest that the expression "automated journalism" should be called into question, that more attention should be devoted to non-English speaking scholarship, that the collective and individual impacts of the technology on media practitioners should be better documented and that well-established sociological theories such as institutionalism and Bourdieu's field theory could constitute two adequate frameworks to study automated journalism practices. Conclusions: This systematic literature therefore provides researchers with an overview of the main challenges and debates that are occurring within the field of automated journalism studies. Future studies should, in particular, make use of institutionalism and field theory to explore how automated journalism is impacting the work of media practitioners, which could help unearth common patterns across media organisations.