# Learning Political DNA in the Italian Senate

## TL;DR

This paper introduces a novel probabilistic method called Political DNA to analyze Italian Senate voting data, revealing hidden political affinities and relationships among senators using sparse learning and information theory.

## Contribution

It proposes a new affinity measure for political analysis based on sparse feature extraction and probabilistic modeling of voting data, advancing social science applications.

## Key findings

- Revealed hidden relationships among Italian senators.
- Introduced the Political Data-aNalytic Affinity measure.
- Demonstrated effectiveness of sparse learning in political data analysis.

## Abstract

Motivated by the increasing interest of the control community towards social sciences and the study of opinion formation and belief systems, in this paper we address the problem of exploiting voting data for inferring the underlying affinity of individuals to competing ideology groups. In particular, we mine key voting records of the Italian Senate during the XVII legislature, in order to extract the hidden information about the closeness of senators to political parties, based on a parsimonious feature extraction method that selects the most relevant bills. Modeling the voting data as outcomes of a mixture of random variables and using sparse learning techniques, we cast the problem in a probabilistic framework and derive an information theoretic measure, which we refer to as Political Data-aNalytic Affinity (Political DNA). The advantages of this new affinity measure are discussed in the paper. The results of the numerical analysis on voting data unveil underlying relationships among political exponents of the Italian Senate.