Measuring Political Polarization: Twitter shows the two sides of Venezuela

TL;DR

This paper introduces a methodology to measure political polarization on social media by modeling opinion propagation and applying it to Twitter data about Venezuela, effectively detecting varying polarization levels.

Contribution

It presents a novel model for estimating opinions from social interactions and a polarization index, validated on Twitter data about Venezuela.

Findings

The methodology accurately detects polarization levels in Twitter conversations.

Results align well with offline data on Venezuelan political opinions.

The approach reveals how network structure influences polarization.

Abstract

We say that a population is perfectly polarized when divided in two groups of the same size and opposite opinions. In this paper, we propose a methodology to study and measure the emergence of polarization from social interactions. We begin by proposing a model to estimate opinions in which a minority of influential individuals propagate their opinions through a social network. The result of the model is an opinion probability density function. Next, we propose an index to quantify the extent to which the resulting distribution is polarized. Finally, we apply the proposed methodology to a Twitter conversation about the late Venezuelan president, Hugo Ch\'avez, finding a good agreement between our results and offline data. Hence, we show that our methodology can detect different degrees of polarization, depending on the structure of the network.