Dominant Disciplinary and Thematic Approaches to Automated Fact-Checking: A Scoping Review and Reflection

Abstract

As artificial intelligence (AI) has become pervasive in journalistic production, the influence of algorithms on media practices has grown. We must always consider the interdisciplinary character of such sociotechnical systems. Otherwise, disciplinary discrepancies might impede the further development of these technologies. This article examines the emerging phenomenon of automated fact-checking in the context of information disorder and the growing demand for scalable solutions for information verification. Here, I identify the dominant disciplinary approaches and themes in research through a scoping review of 199 paper abstracts. My analysis shows that the literature on automated fact-checking is dominated by computer science, while the media perspective remains overlooked. Thematically, abstracts mostly concern the purpose and scope of such systems, their key components, tasks, features, and limits. Based on disciplinary and thematic analysis, I make a distinction between a computational and journalistic understanding of automated fact-checking and offer an interdisciplinary understanding of it. I argue for emphasizing the mundane use of AI technologies instead of striving for the epistemic authority of algorithms by anthropomorphizing them. This study offers the journalism research community new insights into emerging media technologies while suggesting a realistic research agenda for computer scientists by the interdisciplinary conception of automated fact-checking.