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Emotional Framing in Right-Wing Populist Communication on Twitter. A German Dictionary-Based Measurement

Media
Populism
Quantitative
Social Media
Communication
Daniel Thiele
University of Vienna
Daniel Thiele
University of Vienna
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Abstract

Constructing the antagonism that lies at the heart of right-wing populism (Mudde 2004, 2007) – “the people” vs. “the elite” and “others” perceived as threatening – is a highly emotional process (Canovan 1999, Wirz et al. 2018, Pajnik/Sauer 2018). While recent experimental evidence demonstrated the effects of emotional frames like fear and anger in populist communication (Hameleers et al. 2017), such emotional frames remain both undertheorized and overlooked by most quantitative content analyses of real-world communication (Wettstein et al. 2018). The latter is also due to a lack of convenient measurement tools. To fill this gap, the aim of the paper is twofold. Firstly, it conceptually clarifies the role of distinct emotional frames for right-wing populist communication. Secondly, it puts forward dictionary-based measurements of emotional frames (fear, anger and positive emotions) and right-wing populist communication (anti-elitism, people-centrism and nativism), building upon existing work. Both dictionaries are tailored to automated content analysis of Twitter messages from Austrian politicians. Conceptually, the paper integrates a sociological perspective on the role of emotions in right-wing populism (Salmela/von Scheve 2017, 2018) with approaches informed by appraisal theory (Rico et al. 2017, Magni 2017) and a communication perspective on framing (Entman 1993). It argues that expressions of distinct emotions like fear, anger or joy can be regarded as frames (Nabi 2003) that enhance the persuasiveness of a message and ultimately operate as political mobilizers (Benford/Snow 1988). The paper derives hypotheses about differential links between distinct emotional frames and the three core ideological dimensions of right-wing populism – anti-elitism, people-centrism and nativism (Mudde 2004, 2007). While fear frames are expected to co-occur predominantly with nativism and people-centrism, anger is assumed to be directed against out-groups and hence to be associated with anti-elitism and nativism. Positive emotions may foster the perception of a strong in-group (people-centrism) and thereby contribute to a transformation of fear into anger (Salmela/von Scheve 2017, Mackie et al. 2000). These hypotheses are tested using textual Twitter data (n = 24407, timeframe: Jun 2018 – Jun 2019) from 25 political leaders of all political parties in Austria. The central concepts are quantified using a dictionary approach: For measuring populism, the paper uses a novel dictionary developed by Gründl (forthcoming) that has been extended by a dimension for nativism. For quantifying emotional frames, this paper applies and compares several dictionaries that aim to capture emotional expressions in German language (NRC EmoLex, LIWC, ADU). All measurements are validated against a human-coded gold standard (n=300) (Grimmer/Stewart 2013). The paper will present preliminary findings about the co-occurrence of right-wing populist ideological dimensions and emotional frames, about characteristic patterns in the communication of different political parties in Austria and about temporal shifts in these patterns. This contributes to a more comprehensive understanding of the relation between populist content and style (de Vreese et al. 2018). Methodologically, the paper discusses the constructing process and possible applications of the dictionaries. It will also highlight limitations of this approach and will show ways of improvements and further developments of automated content analysis.