Evolution of the political opinion landscape during electoral periods

TL;DR

This study analyzes how political opinions evolved during Argentina's 2015 and 2019 elections by constructing a semantic network from Twitter hashtags, revealing topic dynamics and opinion shifts without predefined categories.

Contribution

The paper introduces a method that detects emergent political topics from social media community structures, tracking opinion evolution during electoral periods.

Findings

Method captures opinion dynamics and topic formation.

Detects reshaping of political landscape between election rounds.

Identifies differences among supporter groups.

Abstract

We present a study of the evolution of the political landscape during the 2015 and 2019 presidential elections in Argentina, based on the data obtained from the micro-blogging platform Twitter. We build a semantic network based on the hashtags used by all the users following at least one of the main candidates. With this network we can detect the topics that are discussed in the society. At a difference with most studies of opinion on social media, we do not choose the topics a priori, they naturally emerge from the community structure of the semantic network instead. We assign to each user a dynamical topic vector which measures the evolution of her/his opinion in this space and allows us to monitor the similarities and differences among groups of supporters of different candidates. Our results show that the method is able to detect the dynamics of formation of opinion on different topics and, in particular, it can capture the reshaping of the political opinion landscape which has led to the inversion of result between the two rounds of the 2015 election.