Analyzing Voting Behavior in Italian Parliament: Group Cohesion and Evolution

Abstract

The roll calls of the Italian Parliament in the current legislature is studied by employing multidimensional scaling, hierarchical clustering, and network analysis. In order to detect changes in voting behavior, the roll calls have been divided in seven periods of six months each. All the methods employed pointed out an increasing fragmentation of the political parties endorsing the previous government that culminated in its downfall. By using the concept of modularity at different resolution levels, we identify the community structure of Parliament and its evolution in each of the time periods considered. The analysis performed revealed as a valuable tool in detecting trends and drifts of Parliamentarians. It showed its effectiveness at identifying political parties and at providing insights on the temporal evolution of groups and their cohesiveness, without having at disposal any knowledge about political membership of Representatives.

Flash Points: Discovering Exceptional Pairwise Behaviors in Vote or Rating Data

“…For instance, [11] uses clustering and PCA to identify cohesion blocs and dissimilarity blocs of voters within the US senate. Similar work was done on the Finnish [20] and the Italian [1] parliaments. An extensive tool was provided by [9] and applied to Swiss government datasets to detect opinion change of parliamentarians based on their expressed opinions before elections and votes cast afterwards.…”

“…1 gives an overview of our approach. Based on an aggregation level set a priori, the method begins by constituting collections of individuals (1). Bi-sets of individuals are identified by a description (2) and their global pairwise behavior is computed (3).…”

Identifying exceptional (dis)agreement between groups

“…For instance, (Jakulin, 2004) uses hierarchical clustering and PCA to identify cohesion blocs and dissimilarity blocs of voters within the US Senate. Similar work was done on the Finnish (Pajala et al, 2004), the Italian (Amelio and Pizzuti, 2012) and the Swiss (Etter et al, 2014) parliaments to study the polarization and cohesion between parliamentarians. Similarly, Grosskreutz et al (2010) investigate the voting behavior of citizens instead of politicians relying on SD.…”

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

“…Applications of structural polarization scores to real networks have spanned many domains, including the studies of political party dynamics [4,37,49,52,65], cohesion and voting behavior [2,9,14,65], political events [12,15,50,58,66], and online echo chambers [12,13,26]. Despite their widespread application, systematic assessments of how well these structural polarization measures perform are rare.…”