The Pragmatics behind Politics: Modelling Metaphor, Framing and Emotion in Political Discourse

Findings of the Association for Computational Linguistics: EMNLP 2020, pages 4479–4488 November 16 - 20, 2020. c⃝2020 Association for Computational Linguistics 4479 The Pragmatics behind Politics: Modelling Metaphor, Framing and Emotion in Political Discourse Pere-Llu´ıs Huguet Cabot1, Verna Dankers1,3, David Abadi2, Agneta Fischer2, Ekaterina Shutova1 1 Institute for Logic, Language and Computation, University of Amsterdam 2 Department of Psychology, University of Amsterdam 3 Institute for Language, Cognition and Computation, University of Edinburgh {perelluis1993, vernadankers}@gmail.com, {d.r.abadi, A.H.Fischer, e.shutova}@uva.nl Abstract There has been an increased interest in mod- elling political discourse within the natural language processing (NLP) community, in tasks such as political bias and misinforma- tion detection, among others. Metaphor-rich and emotion-eliciting communication strate- gies are ubiquitous in political rhetoric, accord- ing to social science research. Yet, none of the existing computational models of political dis- course has incorporated these phenomena. In this paper, we present the first joint models of metaphor, emotion and political rhetoric, and demonstrate that they advance performance in three tasks: predicting political perspective of news articles, party affiliation of politicians and framing of policy issues. 1 Introduction The role of metaphor and emotion in political discourse has been investigated in fields such as communication studies (Weeks, 2015; Mour˜ao and Robertson, 2019), political science (Ferrari, 2007; Charteris-Black, 2009) and psychology (Edwards, 1999; Bougher, 2012). Political rhetoric may rely on metaphorical framing to shape public opinion (Lakoff, 1991; Musolff, 2004). Framing selectively emphasises certain aspects of an issue that promote a particular perspective (Entman, 1993). For in- stance, government spending on the wealthy can be portrayed as a partnership or bailout, spending on the middle class as simply spending or stimulus to the economy and spending on the poor as a give- away or a moral duty, the former corresponding to the conservative and the latter to the liberal point of view (Stone, 1988). Metaphor is an apt framing device, with different metaphors used across com- munities with distinct political views (K¨ovecses, 2002; Lakoff and Wehling, 2012). At the same time, metaphorical language has been shown to express and elicit stronger emotion than literal lan- guage (Citron and Goldberg, 2014; Mohammad et al., 2016) and to provoke emotional responses in the context of political discourse covered by main- stream newspapers (Figar, 2014). For instance, the phrase “immigrants are strangling the welfare system” aims to promote fear of immigration. On the other hand, the experienced emotions may influ- ence the effects of news framing on public opinions (Lecheler et al., 2015) and individual variations in emotion regulation styles can predict different political orientations and support for conservative policies (Lee Cunningham et al., 2013). Metaphor and emotion thus represent crucial tools in political communication. At the same time, computational modelling of political discourse, and its specific aspects, such as political bias in news sources (Kiesel et al., 2019), framing of societal issues (Card et al., 2015), or pre- diction of political affiliation from text (Iyyer et al., 2014) have received a great deal of attention in the NLP community. Yet, …

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