Polarization and hate speech based on fuzzy logic and transformers: the case of the 2023 Spanish general elections

Abstract

Affective polarization in the digital debate of the Spanish presidential election campaign (2023), following the sudden call of the Spanish president on July 23, was measured. Using transformers, topics were detected, and sentiment analysis techniques were applied in the political debate during the elections to measure the emotional valence of the debate. The topics that dominate most of the debate are Candidates (n1 = 17170) and Opposition (n3 = 15327). These topics also show the highest typical polarization deviances. Based on affective polarization, a polarization measure (JDJ) grounded in the fuzzy sets was applied. The topic activism has the highest polarization value, while the topic of voting has the lowest. This analysis highlights a dichotomy that defines the Spanish political reality: the positive image of conventional political participation in the face of the rejection of collective action processes.