ABSTRACT
Media literacy, a vital field of research and educational practice, is attracting considerable scholarly attention, resulting in a burgeoning research literature. While numerous bibliometric studies have sought to capture the key features and themes of this body of literature, its rapid proliferation requires greater scalability and stronger capability to identify and characterize latent topics. In this study we address this gap by offering a computational bibliometric analysis of a corpus of 4,082 research documents on media literacy, spanning the period from 1985 to 2024. Through analysis of the documents’ metadata with natural language processing (NLP) using Latent Dirichlet Allocation (LDA) with Orange3, an open-access data mining software tool, we identify seven principal topics, each represented by a specific set of documents. The topics 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 the assistance of a Large Language Model to generate a short synthetic description based on each topic’s top keywords. We complement our analysis with VOSviewer to produce co-citation maps of publication sources and authors to identify the disciplinary structure of the field, key ML authors, and their research contributions, which focus especially on media literacy education, digital media, behavioural issues, health impacts, and public perceptions.
KEY WORDS
Bibliometric Analysis. ChatGPT. LDA. Media Literacy. Orange3. Topic Modelling. VOSviewer.
DOI
https://doi.org/10.34135/mlar-25-01-01