Characteristics of Smart City Discourse in South Korea: A Policy Mobility Perspective Using Semantic Network Analysis

This study examines how smart city discourse is structurally configured across different contexts from the perspective of policy mobility. To this end, three types of data were analyzed: South Korean policy reports, South Korean academic literature, and global academic literature. Based on these sources, text datasets were constructed and analyzed using text mining-based semantic network analysis to identify key concepts and their relational structures. The results show that while similar keywords appear across datasets, differences are observed in the relative positions and relational patterns of key concepts. In South Korean policy reports, implementation- and operation-related concepts such as “service,” “information,” and “management” exhibit relatively higher centrality. In South Korean academic literature, “planning,” “policy,” “research,” and “technology” appear alongside governance- and actor-related concepts, indicating broader relational configurations. In global academic literature, concepts such as “sustainable,” “social,” “governance,” and “policy” show relatively similar levels of centrality, suggesting the coexistence of multiple dimensions within the discourse. These findings suggest that smart city discourse may be configured differently depending on institutional and discursive contexts, rather than converging into a single uniform structure. However, the observed differences should not be interpreted solely as reflecting national contextual differences, as variations in dataset composition may also have partially influenced the results. By conceptualizing the smart city as a structured policy discourse, this study contributes to understanding how policy-related concepts may be selectively emphasized and reconfigured across contexts. Methodologically, the study demonstrates the applicability of semantic network analysis for examining relational patterns within smart city discourse across different data types and contexts.