This study maps domestic versus international trajectories in metaverse‑game research via text mining of 203 papers published in 2020–2024 (Korean text: 125; international: 78). We applied structural-equivalence (CONCOR) analysis and estimated topics via latent Dirichlet allocation (LDA). The top‑100 terms are visualized using word clouds. International keywords are led by systematic, technology, and rehabilitation/treatment, reflecting a strong emphasis on technological trends and clinical applications, whereas Korean studies foreground content and development alongside education and UX/immersion. Topic modeling yields four overseas themes (therapy/rehabilitation; mental health; digital accessibility; and education–entertainment convergence) and five Korean themes (technology & systems; UX/interface; healthcare & welfare; content; and platform). International work is dominated by literature studies (61/78; 78.2%), whereas Korean research centers on surveys and development (44 each; 35% apiece), with several case studies (20%). On the basis of these findings, we recommend more controlled experiments and meta‑analyses for Korean papers, and greater prototyping and field testing for international ones, in addition to stronger multidisciplinary collaboration.

ABSTRACT
1. Introduction
2. Research Method
    2.1 Data Collection
    2.2 Text preprocessing and keyword extraction
    2.3 Keyword network and centrality analysis
    2.4 Topic modeling and thematic classification
    2.5 Classification and analysis of research methods
3. Research and Discussion
    3.1 Keyword Frequency and Network Centrality in Domestic and International
    3.2 Thematic Classification via Topic Modeling
    3.3 Comparison of Research Methods in Domestic and International Studies
4. Results and Discussion
Acknowledgement
참고문헌

• Ji-Young Na(Namseoul University Cheon-an, Korea) | 나지영 Corresponding author