Mapping Fake News Research in Digital Media: A Bibliometric and Topic Modeling Analysis of Global Trends

This study aims to identify research trends in communication regarding the phenomenon of “fake news” in digital media. Fake news has become a rapidly growing and significant area of research in communication studies in recent years. Published studies were collected from the Science Citation Index Expanded database. The analysis included the annual distribution of publications, citation metrics, leading journals, countries, institutions, and authors. To explore the conceptual structure, topic modeling was conducted using text mining techniques along with DBSCAN and k-means clustering methods. The United States is a leader in the field, both as a producer country and in terms of technology implementation. Vraga, Bode, Tully, Hameleers, and Tandoc are among the most influential authors. The most cited studies specifically focus on misinformation in the health sector and political disinformation and manipulation during elections. Topic modeling analyses show that the literature mainly clusters around health disinformation, political communication, and verification technologies. The findings have important implications for communication policies, media literacy, and fact-checking technologies. Research that systematically examines fake news from a communication perspective, both in performance and conceptual structures, is scarce in the literature. The resulting thematic clusters provide valuable insights for future research.