The Evolution of Demographic Research Changes: Mapping Pre- and Post-Pandemic Shifts with Computational Methods

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Abstract

This study examines how the COVID-19 pandemic has reshaped the thematic landscape of demographic research by using a corpus of around 4,000 peer-reviewed published articles in leading demography and population academic journals. It applies Natural Language Processing (NLP) methods, specifically Latent Dirichlet Allocation (LDA), Dynamic Topic Modeling (DTM) and BERTopic, to compare pre-pandemic, from 2016 to 2019, and post-pandemic, from 2021 to 2024, research trends. The preliminary finding reveals a shift from traditional demographic topics such as fertility and aging toward emerging concerns about social vulnerabilities and health equity. The article demonstrates how global crises play a role of catalyst, reshaping the disciplinary priorities, conceptual framework, and methodological approaches in demography rather than merely disrupting the academic dynamic. By combining computational text analysis techniques with the examination of thematic shifts in demographic research, this study advances understanding of the evolving nature of demographic scholarship and highlights the potential of NLP tools for addressing complex population questions.

Author

• Ruohong Dong, University of Arizona