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Populism and Negative Emotions within the Italian Politics: A Twitter-based Analysis

Contributo in Atti di convegno
Data di Pubblicazione:
2020
Abstract:
While conceptualizing populism as “a cultural-relational performative style” (Moffitt et al. 2016), this paper seeks to explore whether and the extent to which semantic categories of populism and the emotional frames of anger and fear are combined in the tweets posted by selected Italian politicians and political parties on their Twitter account. Methodologically, a combination of quantitative and qualitative approaches was adopted: Content Analysis, to pinpoint recurrent thematic patterns that are relevant to the research purpose; as well as approaches of Multiple Correspondence Analysis, to verify whether the combination of the chosen variables is recurrent within the Twitter corpus purposely built for the analysis. The same tools were used to explore differences in the use of populism and emotional frames on the basis of gender, provenance and the local versus national political activity of the politicians under analysis. Results have showed that there exists a correlation between the use of given semantic categories of populism (i.e. ‘Appeal to people’, ‘Ostracizing the others’, ‘Attacking the elite’) and the frame of anger. The combined use of these categories was mostly detected in the tweeting style of Italian politicians and parties that collocate on the right-wing political spectrum. Moreover, preliminary findings showed a significant difference in the degree of populism depending either on the national or local political activity.
Tipologia CRIS:
4.1 Contributo in Atti di convegno
Keywords:
Twitter; Italian populism; Content Analysis; emotional style
Elenco autori:
Grasso, Arianna; Santelli, Francesco; Carbone, Francesca
Link alla scheda completa:
https://unora.unior.it/handle/11574/226420
Link al Full Text:
https://unora.unior.it//retrieve/handle/11574/226420/150726/CARBONE_GRASSO_SANTELLI_JADT2020.pdf
Titolo del libro:
Proceedings of: JADT 2020: 15es Journées internationales d’Analyse statistique des Données Textuelles
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URL

https://hal.science/hal-03173949
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