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Populism and emotions: a comparative study using Machine Learning
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Populism and emotions: a comparative study using Machine Learning
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Journal Article
Populism and emotions: a comparative study using Machine Learning
2023
Overview
This study aims to unpack the mobilization of emotions in the political discourse of populist and non-populist parties and above all, across ‘varieties of populism’ (right wing vs. left wing or hybrid). Is there an empirical connection between emotions and populism? Are all types of populisms alike with regards to the emotional appeals within their political discourse? Focusing on Italy as a crucial case for populist communication and using a novel methodological approach based on supervised machine learning, it systematically investigates the intensity and trends of specific emotions in political discourses (institutional and informal, i.e. leaders’ speeches) of all Italian political parties over the last 20 years, for a corpus of more than 13,000 sentences analysed. The findings confirm that (i) populists tend to use more (and a broader repertoire of) emotional appeals than non-populist parties; however (ii) overall, there is an increase in the use of these appeals in the Italian political party discourse over time, especially in terms of negative emotions; and, most importantly, (iii) different types of emotions are mobilized by different types of populisms. Right wing populism mainly uses negative emotions while left wing or hybrid populism employs positive emotional appeals. The communication arena (party manifestoes vs. speeches) nevertheless does matter in the degree and types of emotions mobilized by political actors. This study identifies important implications for research on emotional appeals in politics, populist communication and political campaigning, and populist contagion from an emotion-based perspective.
