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The increasing adoption of algorithmic technologies in newsrooms and the ensuing profound transformations in journalism have led to a considerable surge in scholarly research. However, a systematic understanding of the developments within the field of algorithm-driven journalism, which encompasses all application areas of automation and artificial intelligence technologies in journalism, is currently lacking. To this end, this study aims to bridge the existing academic gap by mapping the research landscape of algorithm-driven journalism through an exploratory qualitative and quantitative systematic literature review (2011–2022) that investigates the full spectrum of English-language articles published in peer-reviewed journals across ten databases within the disciplines of communication studies and computer science. The originality of this study relies on its mixed-method and interdisciplinary approach to scrutinising the widespread integration of algorithmic systems in journalism, which has not been systematically synthesised to date. Building upon the results obtained from a corpus of 348 articles, this study (1) provides insights into the characteristics of authorship, (2) identifies research trends, and (3) synthesises potential research directions addressed by scholars. This article is the first of its kind, making a valuable contribution by laying the groundwork for future research endeavours.
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Stored only when both papers are in the corpus — 16 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
Imported from OpenAlex (CC0) — nothing here is generated.