An AI-Assisted Topic Model of the Media Literacy Research Literature

Media literacy, a vital field of research and educational practice, is attracting considerablescholarly attention, resulting in a burgeoning research literature. While numerous bibliometricstudies have sought to capture the key features and themes of this body of literature, its rapidproliferation requires greater scalability and stronger capability to identify and characterize latenttopics. In this study we address this gap by offering a computational bibliometric analysis ofa corpus of 4,082 research documents on media literacy, spanning the period from 1985 to2024. Through analysis of the documents’ metadata with natural language processing (NLP)using Latent Dirichlet Allocation (LDA) with Orange3, an open-access data mining softwaretool, we identify seven principal topics, each represented by a specific set of documents. Thetopics pertain to media publications and online content, critical thinking, youth behaviour,new media skills in education, news and misinformation, health (particularly among females),and communication strategies. We characterize these media literacy research topics with theassistance of a Large Language Model to generate a short synthetic description based on eachtopic’s top keywords. We complement our analysis with VOSviewer to produce co-citation mapsof publication sources and authors to identify the disciplinary structure of the field, key MLauthors, and their research contributions, which focus especially on media literacy education,digital media, behavioural issues, health impacts, and public perceptions.

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