This thesis seeks to challenge the dominant modes of conceiving the empirical link between citizens’ negative perceptions of immigrants and electoral support for Western European radical right parties, and in doing so, to offer a deeper understanding of the dynamics of radical right voting behavior based on an analysis of radical right parties’ online activities. Despite radical right parties' great popularity and important presence online, little scholarly attention has been paid to their activities in an online space. Accordingly, most empirical research on radical right voting behavior has been conducted in an offline context, ignoring the potential role the Internet plays in connecting radical right parties and voters. Building on Norris’s demand-supply framework, I consider the largely ignored factor, citizens' online activities, in my quantitative model and, ultimately, develop formal links between citizens’ anti-immigrant attitudes and electoral support for radical right parties conditional on their level of Internet usage. Thereby, I aim to provide an answer to the following research question: Does citizens’ Internet usage moderate the relationship between anti-immigrant attitudes and radical right voting? Using data from the 9th wave of the European Social Survey (ESS), I test whether voters' high level of Internet consumption strengthens the positive relationship between anti-immigrant attitudes and electoral support for radical right parties in eight Western European countries. The results show that my expectations are strongly supported at the cross-national level and partially confirmed at the national level by Belgium, Germany, and Italy. My findings hold promise for future work in designing more elaborate and practical voting models.
Identifer | oai:union.ndltd.org:uottawa.ca/oai:ruor.uottawa.ca:10393/41587 |
Date | 21 December 2020 |
Creators | Kim, Jia |
Contributors | Stockemer, Daniel |
Publisher | Université d'Ottawa / University of Ottawa |
Source Sets | Université d’Ottawa |
Language | English |
Detected Language | English |
Type | Thesis |
Format | application/pdf |
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