Visualizing a decade of machine translation research (2016–2025): A VOSviewer-based bibliometric analysis

Background and Purpose: Machine translation has undergone rapid transformation over the past decade, driven by advances in neural machine translation and large language models, yet systematic attention to the field’s overall intellectual structure during this critical transition remains limited. This study aims to visualize the evolution of machine translation research from 2016 to 2025, with particular attention to its interdisciplinary development and trends in the post-2022 era of large language models. Methodology: This study presents a bibliometric analysis of machine translation research from a language and translation perspective, based on 582 journal articles published between 2016 and 2025 and indexed in the Web of Science Core Collection. Using VOSviewer, it examines the main characteristics of machine translation publications and key research themes. Findings: The findings show a steady rise in machine translation research output, reflecting growing scholarly interest alongside technological advances. Research is globally distributed but concentrated in Europe and Asia, particularly in Spain and China. Journals in translation studies and computational linguistics account for the largest share of publications and receive the most scholarly attention. However, the field has yet to form a stable core author group, indicating a dispersed and evolving research structure. Five major research themes are identified: technological development and model innovation; human-machine collaboration and application contexts; machine translation quality evaluation; translation studies and industry-related research; and language education with ethical considerations. Contributions: By mapping research trajectories and key contributors, this study offers a solid foundation for advancing future research, emphasizing the need for cross-regional and interdisciplinary collaboration and a balanced integration of technological, human, and ethical dimensions. Keywords: Bibliometric analysis, machine translation, translation studies, visualization, VOSviewer. Cite as: Tang, N., Moindjie, M. A., Lyu, Z., & Wu, S. (2026). Visualizing a decade of machine translation research (2016–2025): A VOSviewer-based bibliometric analysis. Journal of Nusantara Studies, 12(1), 1-19. http://dx.doi.org/10.24200/jonus.vol12iss1pp1-19