Separating Polarization from Noise: Comparison and Normalization of Structural Polarization Measures

نویسندگان

چکیده

Quantifying the amount of polarization is crucial for understanding and studying political in social systems. Several methods are used commonly to measure networks by purely inspecting their structure. We analyse eight such show that all them yield high scores even random with similar density degree distributions typical real-world networks. Further, some sensitive relative sizes polarized groups. propose normalization existing a minimal set tests score should pass order it be suitable separating from noise. The performance increased 38%-220% after classification task 203 we find choice method not as important normalization, which most have better than best-performing before normalization. This work opens up possibility critically assess compare features different measuring structural polarization.

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ژورنال

عنوان ژورنال: Proceedings of the ACM on human-computer interaction

سال: 2022

ISSN: ['2573-0142']

DOI: https://doi.org/10.1145/3512962