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Generative Artificial Intelligence and the Media: A Bibliometric Analysis in Academic Research

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Hacek J.
Media Literacy and Academic Research
|
2025
|
vol. 8
|
issue 1
29-47
EN
Artificial intelligence (AI) is impacting our lives in many ways and is becoming a part of academic research in various fields. Generative artificial intelligence is an AI system that can generate content based on input data. Despite embarrassing attempts a few years ago, it is gradually becoming part of the media landscape. Academic media research is often associated with AI and vice versa. The purpose of this study is to examine academic research in this area over the past decade. We conducted the research using data from the Web of Science database. Using quantitative bibliometric analysis, we examined topics related to AI, media, and consequently media literacy. Our study includes an analysis of co-citations and co-keywords with a description of the most influential studies and journals where studies focusing on AI and media are found. Our results show that academic research here is mainly focused on the technical nature of the use of AI using natural language processing and deep learning, and for media it is mainly the study of texts online and on social networks, along with a focus on fake news and rumour detection. In the last two years, ChatGPT and AI in healthcare are also new topics due to the spread of misinformation and its detection. In conclusion, this study provides a detailed, insightful look at the extensive field associated with academic AI research in the context of media and provides an overview of the areas that dominate it.
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An AI-Assisted Topic Model of the Media Literacy Research Literature

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Entezami Z., Davis C. H., Entezami M.
Media Literacy and Academic Research
|
2025
|
vol. 8
|
issue 1
5-28
EN
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.
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