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Comput."],"published-print":{"date-parts":[[2025,6,30]]},"abstract":"<jats:p>Polarization poses global concerns for social cohesion and stability, making its understanding crucial for effective mitigation measures. In this paper, we introduce an unsupervised, domain-agnostic framework for computationally modeling, extracting, and measuring polarization in digital media. By leveraging Natural Language Processing and Graph Analysis techniques, the proposed framework creates a Polarization Data Model (PDM) that encompasses key elements of Polarization Knowledge (PK), such as entities, fellowships, dipoles, and discussion topics. To evaluate the effectiveness of the framework, we propose a multi-level PK evaluation methodology that assesses its ability to: (i) capture entities\u2019 attitudes toward various topics, (ii) align politically cohesive fellowships with their respective party manifestos, and (iii) identify domain-specific topics along with their degree of polarization. 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