Surge and Absence: Using Bibliometric and Topic Modeling Methods to Trace Global Patterns in Public Opinion Research

This study provides a comprehensive bibliometric and thematic analysis of global scholarly research on public opinion published in SSCI-indexed English-language journals between 2010 and 2024. Drawing on 2,000+ articles retrieved from the Web of Science Core Collection, we employed keyword co-occurrence analysis and Latent Dirichlet Allocation (LDA) topic modeling to identify prevailing research themes, temporal trends, and patterns of regional engagement. Our results reveal distinct topic surges following major political events (e.g., the Arab Spring, the Trump presidency, the COVID-19 pandemic), while certain sensitive issues—such as public opinion on human rights, authoritarianism, or LGBTQ+ rights—appear conspicuously absent in publications from specific countries. This geographic pattern suggests potential self-censorship and structural limitations in national academic discourses. By mapping both the presence and absence of topics across time and space, this study highlights the global inequalities and silences in the scholarly construction of “public opinion,” offering critical insights for future research and academic transparency.