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.

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Background: This article provides a comprehensive picture of automated news’ usage—understood here as the auto-generation of journalistic text through software and algorithms, with no human intervention in-between except for the initial programming at 18 news organisations in Europe, North America and Australia, following a strategic sample inspired by Hallin and Mancini’s (2004) media system typology. Methods: To describe the many ways it is implemented, I rely on Actor-network theory (ANT) so as to distinguish situations where something more is added to automated news systems from those where initial intent is kept and where the software does what it is supposed to do. Semi-structured interviews with editorial staff, executives and technologists were conducted remotely and elements of a netnography were also carried out. Results: Overall, my findings show that the main transformations—or translations—of automated news systems deal with alternate data sources (e.g., news organisations’ internal feeds, crowdsourced material), new affordances that are specifically built for journalists (e.g., in-house self-editing tools, notification streams) and output other than text (e.g., automated audio summaries for voice assistants).      Conclusions: Although these changes lead to greater journalistic professionalisation, they could also make news organisations become too dependent on Big Tech companies for data acquisition and dissemination of automated news products, thus making platforms gain the upper hand in future developments of these systems.