Time-Series Topic and Network Analysis of the Domestic Research Related to Digital Dysfunction

This study aims to analyze research trends related to ‘digital dysfunction’ in the domestic academic journals using text mining techniques. The analysis was conducted across three time periods (2010~2015, 2016~2020, and 2021~2025), following a structured text mining framework that included data collection, keyword extraction, centrality analysis, and topic modeling. First, TF-IDF was applied to identify the key keywords in each period, and network centrality analysis was used to extract the core terms. Second, SNA was conducted based on the top-ranked keywords to visualize the structural relationships among them. Finally, LDA was used to identify the major research topics and analyze their distributions across a set of documents in each time segment.<br/> The analysis revealed that issues such as ‘addiction’ and ‘cyber violence’ were central in the initial period, followed by an expansion towards topics such as ‘digital literacy’ and ‘information ethics’. In recent years, emerging technologies such as the ‘metaverse’, ‘artificial intelligence’, and ‘algorithms’ have become prominent. This study has empirically identified the evolution of core keywords and thematic transitions within the field of digital dysfunction research in response to changes in the digital environment. These findings are expected to provide valuable foundational insights for future academic studies, policy formulation, and societal response strategies.