Exploring Approaches to Low Fertility through Integrated Application of Big Data-based Topic Modeling and System Dynamics: The Case of South Korea

This study examines the multidimensional aspects of low fertility by integrating big data text mining with system dynamics analysis. While previous research primarily utilized macroeconomic, big data discourse, or system dynamics approaches independently, this research combines textual big data analysis and causal loop modeling to address gaps identified in prior methodologies. Specifically, we analyze social discourses and sentiments related to low fertility through text mining of social media data, and then link these qualitative insights with quantitative simulations using system dynamics. Our integrated approach offers a novel methodological framework that enhances understanding of the complex interactions between societal perceptions, policy interventions, and demographic outcomes. The results underscore the importance of capturing both qualitative social trends and quantitative policy feedback loops, providing valuable implications for designing more effective fertility-enhancing policies.