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Multi-polarization in the 9th term of the European Parliament: A network analysis of voting communities
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Orientador(es)
Resumo(s)
Brexit, a global pandemic, the Russian invasion of Ukraine, and record inflation – few
legislative bodies have faced such a cascade of shocks as the European Parliament did during
its 9th term (2019-2024). Using the Bipartite Configuration Model and a set of network
statistics, this dissertation explores how multi-polarization was characterized during this term
by constructing and analyzing co-voting networks across all legislative subjects and within
specific legislative subjects. The results contest binary polarization narratives inherited from
US/UK scholarship by uncovering a multi-polar landscape. In many legislative subjects,
including “Community policies”, “Internal market, single market”, and “External relations of
the Union”, coalitions realign fluidly, forming several voting communities rather than a single
left-right divide. Ideological affinity and group memberships, not nationality, emerge as the
primary forces that bind or separate Members of the European Parliament, reaffirming the
chamber’s transnational character. Two quantitative patterns stand out. First, the Greens/EFA
and The Left display the highest intragroup cohesion, while governing groups – EPP, S&D, and
Renew – often fracture into multiple, issue-driven alliances, suggesting declining centrist
disciplines. Second, a distinct Eurosceptic versus Euroenthusiastic cleavage crystallizes in
matters concerning the “State and evolution of the Union” subject, cutting across economic
and social ideologies and hinting at a budding second dimension of parliamentary conflict.
Beyond advancing methodological practice, this dissertation warns that legislative consensus
in the European Parliament will hinge on navigating a fluid, multi-polar, issue-driven alliance
landscape rather than building stable grand coalitions.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data Science
Palavras-chave
Political polarization European Parliament Co-voting networks backbone Community detection SDG 16 - Peace, justice and strong institutions