A complex network approach to political analysis: Application to the Brazilian Chamber of Deputies

In this paper, we introduce a network-based methodology to study how political entities evolve over time. We constructed networks of voting data from the Brazilian Chamber of Deputies, where deputies are nodes and edges are represented by voting similarity among deputies. The Brazilian Chamber of de...

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Bibliographic Details
Published in:PloS one 2020, Vol.15 (3), p.e0229928-e0229928
Main Authors: Brito, Ana Caroline Medeiros, Silva, Filipi Nascimento, Amancio, Diego Raphael
Format: Article
Language:English
Subjects:
Biology and Life Sciences
Computer and Information Sciences
Computer science
Ecology and Environmental Sciences
Impeachment
Multiparty system
Partisanship
People and places
Physical Sciences
Political parties
Political systems
Politics
Social networks
Social organization
Social Sciences
Studies
Voting
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Summary:In this paper, we introduce a network-based methodology to study how political entities evolve over time. We constructed networks of voting data from the Brazilian Chamber of Deputies, where deputies are nodes and edges are represented by voting similarity among deputies. The Brazilian Chamber of deputies is characterized by a multi-party political system. Thus, we would expect a broad spectrum of ideas to be represented. Our results, however, revealed that plurality of ideas is not present at all: the effective number of communities representing ideas based on agreement/disagreement in propositions is about 3 over the entire studied time span. The obtained results also revealed different patterns of coalitions between distinct parties. Finally, we also found signs of early party isolation before presidential impeachment proceedings effectively started. We believe that the proposed framework could be used to complement the study of political dynamics and even applied in similar social networks where individuals are organized in a complex manner.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0229928