Electronic Thesis and Dissertation Repository

Degree

Master of Arts

Program

Hispanic Studies

Supervisor

Suárez, Juan L.

2nd Supervisor

Quan-Haase, Anabel

Abstract

This thesis investigates the political process in Spain and Catalonia during the Catalan election in December 2017. This regional election was unusual because of the independence process in Catalonia and its repression. Two parties, Ciudadanos (anti-independence) and Podemos (ambiguous position) and their leaders’ activity in Twitter was analyzed. It was explored from three perspectives: social networks, lexical and emotional discourse and ideological polarization. Firstly, social networks were used to see the properties of the support communities of both parties. Interestingly unlike Ps, Ciudadanos’ (Cs) metrics of cohesion showed that political communities of this party in Spain and Catalonia were remarkably well integrated. Secondly, using machine learning techniques, discourse cohesiveness of Ps and Cs’ politicians was analyzed regarding the lexical and emotional content of their messages. The results showed that even though Cs’ politicians were more lexically similar, Ps’ were more similar in terms of emotions. Specifically, the study of emotions in the discourse shed light on populist messages from Cs. This party used anger and disgust to take advantage the polarized political scenario. Lastly, with a sample of users (N=2000) in Twitter, the relationship between dispositional emotions and ideological polarization was investigated. Results showed that users predisposed to anger were significantly more polarized and those predisposed to fear were significantly less polarized. Interestingly, even though predisposition to fear decreased polarization, the interaction between fear and anger significantly increased it. These results have interesting implications regarding the increasing opportunities of politicians to target the electorate based on personal characteristics.

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