Methodology for Measuring Individual Affective Polarization Using Sentiment Analysis in Social Networks

Abstract

Affective polarization has important consequences for societies and institutions. At the institutional level, it hinders agreement among political actors, which damages the stability of the system. At the social level, it increases tensions and conflicts between people, damaging coexistence. Until now, affective polarization has been studied essentially through surveys, which are generally very costly if large and representative samples are to be obtained and in which the answers of the interviewees may not be totally sincere. Through this article, we apply sentiment analysis techniques to measure affective polarization without resorting to surveys, simply by monitoring the non-self-reported behavior of individuals in social networks. To do that, a novel methodology and a new indicator of affective polarization has been proposed using data from social networks. The proposed methodology and new indicator have been applied to the real case study of the regional elections in Spain, specifically to the autonomous Region of Murcia. The application of the methodology has been satisfactory, as well as that of the new indicator of affective polarization, providing a cost-effective way of calculating polarization. The results show that all political groups are polarized to a greater or lesser extent. Furthermore, the results conclude that the winning ideology in the elections, i.e., the right, was the one whose supporters behaved differently from the supporters of other ideologies.

Keywords

• Affective polarization
• lexicon-based techniques
• natural language processing
• sentiment analysis
• social networks