DOI: 10.26650/acin.1891760 ISSN: 2602-3563

Mapping the Methodological Landscape: A Systematic Review of Quantitative Approaches for Measuring Polarization on Social Media

Eda Sönmez, Keziban Seçkin Codal
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Polarization on social media has become a prominent topic in computational social science, as it shapes public discourse, intensifies societal divisions, and influences collective dynamics. Despite the rapid growth of computational approaches, there remains limited consensus regarding how polarization is operationalized, measured, and validated across different analytical frameworks. This study aims to systematically examine the methodological landscape of computational polarization research by analyzing the conceptualization, quantification, and validation of polarization in empirical social media studies. A PRISMA-guided systematic literature review of 70 empirical studies was conducted, retrieved from the Web of Science Core Collection. A structured coding framework, including analytical paradigms, operational definitions, measurement techniques, data sources, and validation strategies. Using this analytical framework, the reviewed studies were examined across three dominant methodological paradigms: network-based, content-based, and hybrid approaches. The analysis demonstrates that these paradigms rely on distinct assumptions, analytical units, and measurement strategies. Network-based approaches primarily capture structural separation through interaction patterns and community structures, whereas content-based approaches examine semantic, affective, and ideological divergence through textual signals. Hybrid approaches integrate relational and content information to capture the complementary dimensions of polarization. However, the findings indicate that these approaches capture different manifestations of polarization rather than measuring a unified construct. Furthermore, the review highlights persistent challenges, including platform-specific data biases, limited temporal analysis, heterogeneous validation practices, and the lack of standardized benchmarks for comparing polarization measures. This review advances understanding of the strengths, limitations, and applicability of computational polarization approaches and highlights future directions for developing more comprehensive, validated, and comparable measurement frameworks.

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