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Communication Dans Un Congrès Année : 2024

All Polarized but Still Different: a Multi-factorial Metric to Discriminate between Polarization Behaviors on Social Media

Résumé

Online polarization has attracted the attention of researchers for many years. Its effects on society are a cause for concern, and the design of personalized depolarization strategies appears to be a key solution. Such strategies should rely on a fine and accurate measurement, and a clear understanding of polarization behaviors. However, the literature still lacks ways to characterize them finely. We propose GRAIL, the first individual polarization metric, relying on multiple factors. GRAIL assesses these factors through entropy and is based on an adaptable Generalized Additive Model. We evaluate the proposed metric on a Twitter dataset related to the highly controversial debate about the COVID-19 vaccine. Experiments confirm the ability of GRAIL to discriminate between polarization behaviors. To go further, we provide a finer characterization and explanation of the identified behaviors through an innovative evaluation framework.
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Dates et versions

hal-04327401 , version 1 (06-12-2023)

Identifiants

Citer

Celina Treuillier, Sylvain Castagnos, Armelle Brun. All Polarized but Still Different: a Multi-factorial Metric to Discriminate between Polarization Behaviors on Social Media. ACM/SIGAPP Symposium On Applied Computing, Apr 2024, Avila, Spain. ⟨10.1145/3605098.3635972⟩. ⟨hal-04327401⟩
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