T he P ragmatics behind P olitics: M odelling M etaphor, F raming and E motion in P olitical D iscourse (2020)

Abstract: There has been an increased interest in modelling political discourse within the natural language processing (NLP) community, in tasks such as political bias and misinformation detection, among others. Metaphor-rich and emotion-eliciting communication strategies are ubiquitous in political rhetoric, according to social science research. Yet, none of the existing computational models of political discourse 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.

## introduction the role of metaphor and emotion in political discourse has been investigated in fields such as communication studies @xcite , political science @xcite and psychology @xcite . political rhetoric may rely on metaphorical framing to shape public opinion @xcite . framing selectively emphasises certain aspects of an issue that promote a particular perspective @xcite . for instance, 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 giveaway or a moral duty, the former corresponding to the conservative and the latter to the liberal point of view @xcite . metaphor is an apt framing device, with different metaphors used across communities with distinct political views @xcite . at the same time, metaphorical language has been shown to express and elicit stronger emotion than literal lan-guage @xcite and to provoke emotional responses in the context of political discourse covered by mainstream newspapers @xcite . for instance, the phrase "immigrants are strangling the welfare system" aims to promote fear of immigration. on the other hand, the experienced emotions may influence the effects of news framing on public opinions @xcite and individual variations in emotion regulation styles can predict different political orientations and support for conservative policies @xcite . 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 @xcite , framing of societal issues @xcite , or prediction of political affiliation from text @xcite have received a great deal of attention in the nlp community. yet, none of this research has incorporated the notions of metaphor and emotion in modelling political rhetoric. we present the first joint models of metaphor, emotion and political rhetoric, within a multi-task learning (mtl) framework. we make use of auxiliary learning, i.e. training a model in more than one task to improve the performance on a main task. we experiment with three tasks from the political realm, predicting (1) political perspective of a news article; (2) party affiliation of politicians from their social media posts; and (3) framing dimensions of policy issues. we use metaphor and emotion detection as auxiliary tasks, and investigate whether incorporating metaphor or emotion-related features enhances the models of political discourse. our results show that incorporating metaphor or emotion significantly improves performance across all tasks, emphasising the prominent role they play in political rhetoric. ## related work modelling political discourse encompasses a broad spectrum of tasks, including estimating policy positions from political texts @xcite , identifying features that differentiate political rhetoric of opposing parties @xcite or predicting political affiliation of twitter users @xcite @xcite . deep neural networks have been widely used to model political perspective, bias or affiliation at document level: @xcite used a recurrent neural network (rnn) to predict political affiliation from us congressional speeches. @xcite identified the political perspective of news articles using a hierarchical long short-term memory (lstm) and modelled social media user data with graph convolutional networks (gcn). lastly, a recent shared task presented a multitude of deep learning methods to detect political bias in articles @xcite . framing in political discourse has gained some attention recently. @xcite trained tree-rnns to classify framing of policy issues in news articles. @xcite used a weakly supervised approach to identify tweet-level frames and used probabilistic soft logic on language and social-based features. hartmann et al. (2019 approaches predicting emotions for a given text typically adopt a categorical model of discrete, prototypical emotions, e.g. the six basic emotions of @xcite . early computational approaches employed vector space models @xcite or shallow machine learning classifiers @xcite . examples of deep neural methods are the recurrent model of @xcite , who classified 24 fine-grained emotions, and the transformerbased sentibert architecture of @xcite . computational research on metaphor has mainly focused on detecting metaphorical language in text. early research performed supervised classification with hand-engineered lexical, syntactic and psycholinguistic features @xcite @xcite @xcite . alternative approaches perform metaphor detection from distributional properties of words @xcite or by training deep neural models @xcite . @xcite developed a joint model of metaphor and emotion by fine-tuning bert in an mtl setting. ## methods we employ the robustly optimized bert approach (roberta-base) presented by @xcite and use the implementation by @xcite . roberta contains twelve stacked transformer layers and assumes an input sequence to be tokenised into subword units called byte-pair encodings (bpe). a @xmath1, to compute a contextualised sequence representation. our tasks are defined at three levels of the linguistic hierarchy. the auxiliary tasks of metaphor detection and emotion prediction are defined at word and sentence level, respectively, while the main political tasks are defined at document level. for word-level metaphor identification, the subword encodings from roberta's last layer are processed by a linear classification layer. a word is considered metaphorical provided that any of its bpes was labelled as metaphorical. we assume the bpe from inflexions unlikely to cause a word to be incorrectly labelled as metaphorical. for the sentence-level emotion prediction task and the document-level tasks of political affiliation and framing, @xmath2. for political perspective in news articles, the average document length exceeds the maximum input size of roberta. we, therefore, split its documents into sentences and collect them in a maximum of 5 subdocuments with up to 256 subwords. after applying roberta to the subdocuments, @xmath3. a model schematic is shown in figure 1 . all task models use the cross-entropy loss with a sigmoid activation function. for political perspective detection, the loss function includes class weights to account for class imbalance. ## discussion political perspective and affiliation for the political perspective detection task, the performance improvements of mtl models stem mostly from improved predictions for the right-wing class. example 1 of for political affiliation, improvements from auxiliary tasks are due to a more accurate identification of the class of democrats. according to @xcite , liberals are more susceptible to emotions, which could in part explain this result. appendix a.2.2, figure 2 visualises the performance across the political spectrum, from which we infer that politicians at the centre are harder to distinguish, and those on the left are better identified by our mtl models. we explored the emotions predicted by the mtl model in politicians' posts, as shown in framing in case of the framing task, mtl with metaphor prediction yielded the largest improvements for the frames of security and defence, morality and fairness and equality, particularly in articles on the metaphor-rich topics of immigration, guncontrol and death penalty. we automatically annotated metaphorical expressions in these articles to conduct a qualitative analysis. we observed that correct identification of linguistic metaphors often accompanies correct frame classification by the mtl model. examples of such cases are shown in ## conclusion in this paper, we introduced the first joint models of metaphor, emotion and political rhetoric. we considered predicting the political perspective of news, the party affiliation of politicians and the framing dimensions of policy issues. mtl using metaphor detection resulted in significant performance improvements across all three tasks. this finding emphasises the prevalence of metaphor in political discourse and its importance for the identification of framing strategies. joint learning with emotion yielded significant performance improvements for the political perspective and affiliation tasks, which suggests that the use of emotion is an important political tool, aiming to influence public opinion. future research may explore further tasks such as emotion and misinformation detection, which social scientists have found to be inter-related, and deploy more advanced mtl techniques, such as soft parameter sharing. our code is publicly available at github.com/littlepea13/mtl political discourse. emotions facebook post gold label emotions mtl stl anger, disgust and fear last week, i held a congress on your corner event in frankfort. monica was upset by the recent deal between the united states, our global partners, and iran. the deal provides @xmath088 billion and that the plan would have premiums 5-7% lower than private plans, which would increase competition in the marketplace and result in substantial savings for individuals, families, and employers purchasing health insurance through an exchange.