A New Method and Application for Studying Political Text in Multiple Languages

The Pennsylvania State University The Graduate School

THE RADICAL RIGHT IN PARLIAMENT: A NEW METHOD AND APPLICATION FOR STUDYING POLITICAL TEXT IN MULTIPLE LANGUAGES

A Dissertation in Political Science

by Mitchell Goist Submitted in Partial Fulﬁllment of the Requirements for the Degree of Doctor of Philosophy

May 2020 ii

The dissertation of Mitchell Goist was reviewed and approved* by the following:

Burt L. Monroe Liberal Arts Professor of Political Science Dissertation Advisor Chair of Committee

Bruce Desmarais Associate Professor of Political Science

Matt Golder Professor of Political Science

Sarah Rajtmajer Assistant Professor of Information Science and Tecnology

Glenn Palmer Professor of Political Science and Director of Graduate Studies iii

ABSTRACT

Since a new wave of radical right support in the early 1980s, scholars have sought to understand the motivations and programmatic appeals of far-right parties. However, due to their small size and dearth of data, existing methodological approaches were did not allow the direct study of these parties’ behavior in parliament. Using a collection of parliamentary speeches from the United Kingdom, Germany, Spain, Italy, the Netherlands, Finland, Sweden, and the Czech Re- public, Chapter 1 of this dissertation addresses this problem by developing a new model for the study of political text in multiple languages. Using this new method allows the construction of a shared issue space where each party is embedded regardless of the language spoken in the speech or the country of origin. Chapter 2 builds on this new method by explicating the ideological appeals of radical right parties. It ﬁnds that in some instances radical right parties behave similarly to mainstream, center-right parties, but distinguish themselves by a focus on individual crime and an emphasis on negative rhetorical frames. The chapter further illustrates how radical right populist appeals differ from leftist populist appeals, through the latter’s emphasis on material deprivation. Finally, Chapter 3 shows how mainstream parties react to increases in radical right support, ﬁnding that they engage in rhetorical concessions, attempting to mimic the radical right’s rhetoric while granting fewer substantive concessions. This chapter provides evidence for a ”coarsening” of debate that can arise after radical right entry into parliament. iv

Contents

List of Figures vii

List of Tables x

1 Introduction 1

2 Analysis of Political Texts in Multiple Languages 5 2.1 Introduction ...... 5 2.2 Background ...... 6 2.3 A multilingual text analytics pipeline ...... 8 2.3.1 Modeling topical semantics with word embeddings ...... 9 2.3.2 Aligning vector space across languages with Procrustes analysis . . . . 11 2.3.3 Interpretable embeddings with decomposition and rotation ...... 14 2.3.4 Document scoring ...... 16 2.4 Parliamentary speech data and preprocessing ...... 19 2.5 Validity ...... 20 2.5.1 Content validity of cross-lingual topics ...... 20 2.5.2 Validity of linear political semantics ...... 30 2.6 Common Space Understandings of Political Competition ...... 33 2.6.1 What is the shared landscape of interparty competition? ...... 33 2.6.2 Can this improve existing understandings of party families? ...... 35 2.7 Conclusion ...... 40 v

3 The Radical Right in Parliament: Anti–immigration, Authoritarianism, and Pop- ulism 42 3.1 Introduction ...... 42 3.2 Literature Review and Research Questions ...... 45 3.2.1 Contributions and Research Design ...... 48 3.3 Methodological Approach ...... 50 3.3.1 ICA vs. SVD ...... 52 3.4 Immigration ...... 54 3.4.1 Immigration ...... 55 3.4.2 Religion ...... 63 3.5 Authoritarianism ...... 69 3.5.1 Crime ...... 70 3.5.2 Public Health ...... 76 3.6 Populism ...... 82 3.6.1 Europe ...... 83 3.6.2 Rhetorical Strategies ...... 89 3.7 Conclusion and Future Research ...... 95

4 The Radical Right in Competition 97 4.1 Issue entrepreneurs and responses from mainstream parties ...... 97 4.1.1 Theoretical expectations ...... 101 4.2 Issue attention and ownership ...... 107 4.3 Impact of radical right parties ...... 113 4.3.1 All parties ...... 114 4.3.2 Disaggregated Effects ...... 116 4.4 Conclusion ...... 123

Bibliography 125

Appendices 141 vi

A Chapter 2 142 A.1 Substantive Keywords ...... 142 A.2 Rhetorical keywords ...... 153

B Chapter 3 162 B.1 Attention to subtopics over time ...... 162 B.1.1 Immigration ...... 162 B.1.2 Religion ...... 167 B.1.3 Crime ...... 173 B.1.4 Public Health ...... 179 B.1.5 Europe ...... 184

C Chapter 4 189 C.1 Government / Opposition, all parties ...... 189 C.2 Liberals and Christian Democrats ...... 189 vii

List of Figures

1.1 Master Legend for Party Graphics ...... 4

2.1 Procrustes alignment using polysemic bilingual dictionaries...... 13 2.2 Rotation of embeddings to simple structure...... 17 2.3 Attention to Energy over time ...... 25 2.4 Attention to Inequality over time ...... 26 2.5 Attention to Business over time ...... 27 2.6 Attention to #Failure over time ...... 28 2.7 Dimensions of weighted SVD of topic space by party ...... 34 2.8 Assessing the ﬁdelity and clarity of party family memberships ...... 36 2.9 Varimax rotation of substantive and rhetorical topics ...... 39

3.1 Scores for Asylum over time for each country ...... 58 3.2 Attention to Vulnerable Groups within the Immigration topic ...... 60 3.3 Immigration scaling ...... 62 3.4 Attention to Immigration within the Religion topic ...... 65 3.5 Attention to Christianity within the Religion topic ...... 67 3.6 Religion scaling ...... 68 3.7 Attention to Prison within the Crime topic ...... 72 3.8 Attention to Offences within the Crime topic ...... 74 3.9 Crime scaling ...... 75 3.10 Attention to Crime within the Public Health topic ...... 79 3.11 Public Health scaling ...... 81 viii

3.12 Attention to Others within the EU topic ...... 85 3.13 Attention to New Members within the EU topic ...... 87 3.14 EU scaling ...... 88 3.15 Failure scaling ...... 91 3.16 Nonsense scaling ...... 93 3.17 Representation scaling ...... 94

4.1 Oppositional topics: capitol ∼ labor ...... 104 4.2 Oppositional topics: immigration ∼ environment ...... 105 4.3 Relative attention for Nationalist parties compared to others ...... 108 4.4 Issue attention by party family, mainstream parties ...... 111 4.5 Issue attention by party family, challenger parties ...... 112 4.6 Attention to topics for mainstream parties by presence of radical–right parties . 115 4.7 Relative attention to topics, by radical–right seated ...... 116 4.8 Relative attention to topics, by radical–right seated (Liberal and Christian Demo- crat) ...... 118 4.9 Relative attention to topics, by radical–right seated (Leftist and Ecological) . . 119 4.10 Relative attention to topics, by radical–right seated (Social–Democrats / Oppo- sition — Government) ...... 121 4.11 Relative attention to topics, by radical–right seated (Conservatives / Opposition — Government) ...... 122

B.1 Attention to Internally Displaced Persons over time ...... 163 B.2 Attention to Applications over time ...... 164 B.3 Attention to Terrorism over time ...... 165 B.4 Attention to EU over time ...... 166 B.5 Attention to Charity over time ...... 168 B.6 Attention to Charity over time ...... 169 B.7 Attention to Violence over time ...... 170 B.8 Attention to Gender over time ...... 171 B.9 Attention to Schools over time ...... 172 ix

B.10 Attention to Courts over time ...... 174 B.11 Attention to Rights over time ...... 175 B.12 Attention to Police over time ...... 176 B.13 Attention to Prison over time ...... 177 B.14 Attention to Political Violence over time ...... 178 B.15 Attention to Alcohol over time ...... 180 B.16 Attention to Animals over time ...... 181 B.17 Attention to Children over time ...... 182 B.18 Attention to Consumers over time ...... 183 B.19 Attention to Markets over time ...... 185 B.20 Attention to Eurozone over time ...... 186 B.21 Attention to NATO over time ...... 187 B.22 Attention to Threats over time ...... 188

C.1 Relative attention to topics for government and opposition, by radical right seated 190 C.2 Relative attention to topics for Liberals and Christian Democrats, by radical right seated ...... 191 C.3 Relative attention to topics for Christian Democrats, by radical right seated, disaggregated by opposition status ...... 192 C.4 Relative attention to topics for Christian Democrats, by radical right seated, disaggregated by opposition status ...... 193 x

List of Tables

2.1 Embeddings under different context windows...... 12 2.2 Corpus meta-data ...... 19 2.3 Politically substantive topics ...... 22 2.4 Rhetorical topics ...... 23 2.5 Semantic party analogies ...... 31 2.6 Consistency of Manifesto Project Party Families in Vector Space ...... 38

3.1 Ideological pillars, topics, and sub–topics ...... 44 3.2 A spatial theory of nationalist issues ...... 49 3.3 SVD vs. ICA on embedding space with a minimum term count of two . . . . . 54 3.4 Immigration keywords ...... 57 3.5 Religion keywords ...... 64 3.6 Crime keywords ...... 71 3.7 Public Health keywords ...... 77 3.8 EU keywords ...... 84

A.1 Pensions, Terrorism, Representation, and Professions keywords ...... 142 A.2 Inequality, Discrimination, Health, and Agriculture keywords ...... 143 A.3 Envrionment, International Crises, Private / Public, and Banks keywords . . . . 144 A.4 Families, Public Health, Budget, and Energy keywords ...... 145 A.5 Global (Aid/Climate), Macroeconomy, Media, and Courts / Constitutional keywords ...... 146 A.6 Jobs, Rights, Prison, and Maritime keywords ...... 147 xi

A.7 Local / Regional, Religion, Bureaucracy, and Transport keywords ...... 148 A.8 Business, Education, Housing, and Crime keywords ...... 149 A.9 Science / Research, Sport, Taxes, and Workers keywords ...... 150 A.10 Defense, Disabilities, OECD / Trade, and Agencies / Bodies keywords . . . . . 151 A.11 Immigration, EU, History / Heritage, and Universities keywords ...... 152 A.12 #Failure, #Studies, #Decisions, and #Statistics keywords ...... 154 A.13 #Compliance, #Nonsense, #Groups, and #Issues keywords ...... 155 A.14 #I Am, #Objectives, #Standards, and #Initiatives keywords ...... 156 A.15 #Reasons, #Timetable, #Disaster, and #Quotes keywords ...... 157 A.16 #Problems/Solutions, #Questions/Answers, #Comparisons, and #Skepticism keywords ...... 158 A.17 #Praise, #Differences, #Alternatives, and #Costs keywords ...... 159 A.18 #Uncertainty, #Change, #My, and #Rules keywords ...... 160 A.19 #Procurement and #Transparency keywords ...... 161 1

Chapter 1

Introduction

Despite the explosion of interest and large body of scholarship aimed at understanding radical right parties in the past decades, there remain certain seemingly intractable problems. A primary issue, one that affects the study of all niche parties, is that the parties themselves are quite small and not often represented in parliament. This makes comparative studies quite difﬁcult. This is doubly true for scholarship that employs text–based methods to study these parties. How can a researcher do comparative work if they are limited to, say, a single radical right party represented within a parliament for only a handful of years?

This study seeks to rectify this issue by creating a model for studying political text in multiple languages. While there are, of course, many uses of such a model for studying any political phenomenon that is expressed in multiple languages, it is particularly useful for the study of smaller parties that are infrequently represented. By embedding the speeches of many small parties within the same common space, this method allows scholars to trace similarities and contrast differences amongst the radical right as a whole, instead of being limited by common languages or national boundaries.

Chapter 2 lays out this method for multilingual text analysis. First, we describe originally col- 2 lected and previously curated data on eight European countries: The United Kingdom, Swe- den, Spain, The Netherlands, Italy, Finland, Czech Republic, and Germany. We then develop a pipeline for taking these languages and embedding the tokens within a common geometric space, using word2vec and neural network powered Procrustean analysis. We then take this common space, and apply a linear transformation and rotation procedure, similar to those used in psychometric analysis, to transform this common space into politically relevant substantive and rhetorical topics. We validate these topics carefully, demonstrating face validity and propos- ing a new task of political semantic analogies. The chapter concludes by demonstrating that our semantic space accurately recreates scholarly understandings of party families with no labelled data and minimal human supervision.

Chapter 3 applies this model to the study of radical right parties. Specifically, it investigates the three tenets of radical right ideology: anti–immigration; authoritarianism; and populism. Borrowing from the insights in Chapter 2, it investigates these issues through a spatial perspective, finding that a typical configuration arises in for anti–immigration and authoritarianism: radical right parties at an extreme end of a pole, followed closely by Conservative parties, and opposed by smaller (i.e. non–governing) Christian parties. Populist political topics present a different structural configuration, with Leftists and Nationalists aligned on one axis, but with Leftists separated by their emphasis on material impacts. The data also suggest that the radical right distinguishes itself from Conservatives and other right–wing parties through an emphasis on topics that inculcate a sense of personal fear, such as violent crimes and offences, whereas Conservatives focus on traditional law and order.

Finally, Chapter 4 presents the dynamics of inter–party competition, and the impact of the radical right being seated in parliament. I find that, contrary to expectation, Conservative parties do not adopt an accommodative strategy, instead adopting a dismissive strategy by de–emphasizing topics associated with the far–right. I also find strong evidence that Social Democratic parties adopt an adversarial strategy, engaging in a direct adversarial strategy, where they contest the 3 radical–right’s positions on their own issue terrain, or a soft adversarial strategy, where they emphasize adjacent topics where they have stronger position. As a counterpart to this adversarial strategy, I find that center–left parties will de–emphasize their core economic issues, which, coupled with their adversarial strategies, produces a more “New Politics” alignment that is less reliant on the traditional economic left–right cleavage. 4

Figure 1.1: Master Legend for Party Graphics

Note: Since plots will frequently reference party family and government status through color and background, this legend goes over the conventions that will be repeated throughout the paper. 5

Chapter 2

Analysis of Political Texts in Multiple Languages

2.1 Introduction

Perhaps the most fundamental barrier to the comparative study of politics is the curse of Babel. One individual scholar can only know a limited number of languages and is limited in the primary sources she can leverage. As a result, the vast majority of comparative politics by individual scholars is either country or region speciﬁc, or compares relatively “pre-quantiﬁed” data like votes in elections or dollars of foreign direct investment. Beyond that, comparative scholarship on a true multinational scale often requires the construction and maintenance of complex and expensive cross-national teams of experts, as with the Comparative Manifestos Project (Merz, Regel and Lewandowski 2016), the Comparative Policy Agendas Project (Baumgartner, Green- Pedersen and Jones 2006), the Varieties of Democracy (V-DEM) project Lindberg et al. (2014), or the Comparative Study of Electoral Systems (CSES) project (Klingemann 2009).

Google Translate notwithstanding, text analytics and statistical NLP methods to date offer lim- 6 ited relief. Text analytic techniques generally leverage repeated patterns, like word co-occurrence. But when there are few words or other linguistic structures in common, these techniques have little to offer.

Building on recent advances in transfer learning and multilingual word embeddings (and old advances in multivariate statistics) we propose a new method for analyzing multilingual corpora, which does not rely on translation to a common language or a polyglot researcher or team. Further, we propose novel methods for extracting topics and scales from multilingual corpora. We demonstrate the utility of these methods to comparative politics through analyses of a corpus of parliamentary speeches from eight European countries. Our substantive objective is to reveal a common, politically relevant semantic space. This would allow us to embed words or any combination thereof (i.e. collections of speeches from parties, speakers, years, countries, etc.) within a topical space that represents the scope of discourse in the parliaments under study. While not a perfect approximation of ideology, due to the strategic and institutional constraints of the parliamentary setting, this common semantic space can provide a lower dimensional representation of political discourse across multiple parliaments, all constructed with very little human supervision or labor.

2.2 Background

With the rise of data availability and computational power, social scientists have increasingly turned to analyzing the vast body of textual data produced through human interaction (Gentzkow, Kelly and Taddy 2017; Wilkerson and Casas 2017; Evans and Aceves 2016; Monroe and Schrodt 2008). Research programs focus on a wide array of text sources such as surveys (Roberts et al. 2014), parliamentary speeches (Tzelgov 2014), election manifestos (Laver, Benoit and Garry 2003), campaign materials (Catalinac 2018), judicial decisions (Clark, Lax and Rice 2015), committee transcripts (Schonhardt-Bailey 2012), international organizations (Miller 2013), press 7 releases (Grimmer 2010a), news reports (Schrodt, Davis and Weddle 1994; Beieler et al. 2016), and social media (King, Pan and Roberts 2013). Scholars have used these data sources to gain insight into political polarization (Monroe, Colaresi and Quinn 2008), representativeness and accountability (Lowe et al. 2011a), issue ownership and attention (Wilkerson and Casas 2017; Kluver¨ and Sagarzazu 2016; Quinn et al. 2010), political afﬁnity (Barbera´ 2015), and rhetorical style (Baturo and Mikhaylov 2013). Recent studies have extended textual analysis to sources in multiple languages (Dai and Radford 2018).

Perhaps the most commonly used text analytic techniques in social science are unsupervised ways to create lower dimensional representations. An example of lower dimensional representations are found in word embeddings, employed by Dai (N.d.). Typically, the lower dimensions represent substantively interesting groupings of words and/or documents, which can in turn be used as measures of some socially relevant phenomenon (Quinn et al. 2010). Methods of this type are often referred to as “topic models,” the most commonly used of which is Latent Dirich- let Allocation (LDA) (Blei, Ng and Jordan 2003; Blei and Lafferty 2006). There have been many variants and related models proposed since – e.g., Blei and Lafferty (2006); Li and Mc- Callum (2006); Gallagher et al. (2017) – including several from political science (Quinn et al. 2010; Grimmer 2010a), with the Structural Topic Model (STM) of Roberts et al. (2014) the most widely adopted.

While many commonly used statistical NLP methods are essentially language agnostic, they depend on repeated words or other patterns. As a consequence, they do not lend themselves well to comparisons across languages. One potential solution to this, suggested by Lucas et al. (2015) and recently explored extensively by de Vries, Schoonvelde and Schumacher (2018), is to ﬁrst translate all text into English, using existing machine translation implementations by Microsoft or Google. Practically speaking, however, machine translation for large corpora (such as the parliamentary speeches used in this paper) is cost prohibitive.1 Scientiﬁcally speaking,

1Various tricks have been used to avoid this cost, but we are unaware of any such tricks that respect the translation services’ Terms of Service. 8 such systems are black boxes, designed and tweaked in unknown and uncontrollable ways, with commercial objectives that differ from ours.

2.3 A multilingual text analytics pipeline

We describe here a method to analyze corpora in multiple languages and create a model of those text data in a common parameter space. In our case, the examples arise from our studies of representation and party competition as evidenced in parliamentary speech. Our method allows us to, for example, create a single multilingual topic model to characterize attention to political issues across national parliaments and within party families. The primary novelty in our pipeline is not in the individual elements themselves – the workhorse in most steps here is the well-known singular value decomposition (SVD) – but in our novel combination of approaches and interpretations from disparate sources in the NLP, machine learning, statistics and social science communities. The algorithm that we develop has the following steps, corresponding to the sub–sections below:

1. Model “topical” semantics of language in each language or context. We train word embeddings on each monolingual corpus, with an unusual variant of word2vec designed to estimate a vector space that represents the semantics of parliamentary political topics.

2. Align vector spaces in a common space. We use Procrustes analysis to align our embeddings to minimize errors in alignment of word pairs in bilingual dictionaries.

3. Make the common space interpretable. We model this step on an old-school social scien- tiﬁc pipeline from psychometrics – “the Little Jiffy” – which represents the vector space through a matrix decomposition, and then rotates that space to maximally interpretable (and sparser) “simple structure.” 9

4. Reduce the space to dimensions of interest (e.g., in our present application, dimensions with policy, ideological, and/or rhetorical content).

5. Score documents and groups of documents (by, for example, political party or party family) for topical and other semantic content based on the vector space locations of the words used in the documents. Speciﬁcally, we characterize a document as a vector of regularized positive pointwise mutual information (PPMI) between words and the document and calculate its cosine with a semantic vector of interest (representing a topic, for example) in the space.

2.3.1 Modeling topical semantics with word embeddings

Word embeddings and word2vec

First introduced by Mikolov et al. (2013), word embeddings have shown great success in a variety of NLP tasks (Chen et al. 2013). Contrasted with term-document or term co-occurrence matrices, which represent terms in a high dimensional and sparse feature space, the goal here is to represent words in a dense, lower dimensional space. While they are most frequently used as features in supervised models, the embeddings themselves have interesting properties, the most famous of which is that basic linear vector operations reﬂect semantic relationships like

2 Vking −Vman +Vwoman ≈ Vqueen.

Word embeddings can be derived in different ways. For this paper, we use the skipgram model proposed by Mikolov et al. (2013), and its original implementation in “word2vec.” It is beyond our scope here to go through each step of the model formally, but a brief description is as follows. For every word, w in the corpus, we deﬁne a context window of arbitrary length j composed of

2They also maintain biases inherent in the training data. These may be undesirable human biases, such as Vdoctor − Vman + Vwoman ≈ Vnurse (Caliskan, Bryson and Narayanan 2017), or biases based on a shift of time or context that alters the semantics of a word. For example, the GloVe word embeddings (Pennington, Socher and Manning 2014) trained on the Wikipedia corpus in 2014, identify the nearest neighbors to the word Trump as words like developer and casino, which would presumably no longer be the case. 10

words wi± j. For any given context-word, wc, we are attempting to maximize the likelihood of p(wc|wi), or the likelihood that we observe the context word given our target word. This probability is estimated with the logistic function.3

An immediate problem with estimating this probability arises because, by definition, we only observe word–context pairs that occur, and there are infinitely many pairs that did not occur. In order to estimate the model, Mikolov et al. (2013) employ “negative sampling,” which resamples other words from the document at random that do not appear in the context window. This also functions as a form of regularization similar to a Bayesian prior. The model is trained using a shallow neural network with a single hidden layer. The output layer, as discussed above, is of no direct interest. The word embeddings themselves are in the hidden layer, or the weights assigned to each word by the model, with the rows equal to the number of words in the vocabulary, and columns equal to the number of neurons specified in constructing the neural network.4

Size of the context window

In the original paper, Mikolov et al. (2013) use a context window of 10, and most implementations don’t stray far from this number. Jurafsky and Martin (2018) state:

The size of the window used to collect counts can vary based on the goals of the representation, but is generally between 1 and 8 words on each side of the target word (for a total context of 3-17 words). In general, the shorter the window, the more syntactic the representations, since the information is coming from immediately nearby words; the longer the window, the more semantic the relations.

We extrapolate that insight wildly beyond standard practice and use windows of 300 that stop at document boundaries. Our substantive objective involves ﬁnding (at least) ministry-level

3Called the “softmax” function by Mikolov et al. (2013). 4Researchers typically use 100-300 neurons, with larger dimensions recommended for larger corpora. In this paper we use 300. 11 semantic/topical associations, and we are less interested in more short-range syntactic associations. Most of the papers that employ word embeddings are seeking to create a “neutral” model of basic semantic relationships that is portable to a variety of contexts. Typical practice in fact is to not be concerned about the separation of text into discrete “documents” and to slide the context window across document boundaries. Our goal, on the other hand, is to use these embeddings as a document-level feature to uncover politically relevant behaviors tied to speciﬁc speakers or aggregations of speakers like political parties.

To illustrate the effect of window size on the character of semantic relationships uncovered, we applied the model to a variety of window sizes. Table 2.1 shows groupings of associated words found by applying word2vec with varying window size to our English-language corpus of House of Commons speeches.5 The small window examples appear to capture words with very narrow contexts. The ﬁrst is present participle verbs; the second are modal verbs (verbs that need another verb, like can or will need something like do) or adjectives that precede an inﬁnitive form verb like to do. As the size of the context window increases, we capture more long-range relationships, such as those captured by pairs of terms, particularly nouns, that frequently appear in the same paragraph as their most similar words, but rarely in the same three-word window.

2.3.2 Aligning vector space across languages with Procrustes analysis

Constructing word embeddings for each language provides a reduced dimensionality representation of words in that language. But without shared words, there is nothing for most models to do but split the corpus by language and call it a day. Building off the insight from Mikolov, Le and Sutskever (2013) that word embeddings across languages share similar geometric structure, scholars have devised different methods to align these similar geometric spaces with one another, allowing vector operations as multilingual as Vde ehemann - Ves padre + Vit madre = Vnl echtgenote

5These speciﬁc examples are words that load highly on a dimension after the rotation procedure we describe further below, but at this stage, it is sufﬁcient to think of them just as words that are near one another in the embedding space. 12

Table 2.1: Embeddings under different context windows.

Window size: 3 Window size: 30 Window size: 300 putting able effective crimes diseases tourism bringing can deliver offences disease visitors taking unable improve offence vaccine museums giving trying strengthen prosecutions obesity tourist introducing prepared improving murder screening citizenship providing will efﬁcient criminals diabetes Media looking willing sustainable crime pregnancy holiday making wants develop arrested HIV holidays talking want delivering cases medical music publishing happy ensuring prosecution babies Olympics

Note: Examples of associated terms in embeddings estimated by word2vec on a House of Com- mons corpus. Smaller windows tend to capture syntactical relations; larger windows tend to capture more topical relations.

(Vhusband - Vf ather + Vmother = Vwi f e, from German to Spanish to Italian to Dutch) (Duong et al. 2017).

For this paper, we use an approach devised by Mikolov, Le and Sutskever (2013), reﬁned by Xing et al. (2015) and implemented by Conneau et al. (2017) in the Facebook Research MUSE project. They motivate the blending of two embeddings spaces as an orthogonal Procrustes problem. Procrustes analysis seeks to map one set of points onto a reference set of points, as with face recognition when the camera and subject are not oriented in exactly the same way. In this case, the points we wish to map as closely as possible are semantic equivalences deﬁned by bilingual dictionaries of word pairs.

Figure 2.1 illustrates the logic. We have word embeddings, locations or vectors for words in a “low-dimensional” (e.g., 300-dimensional) space independently derived from English and Span- ish corpora. We believe the semantic structure is broadly the same, so the problem is to ﬁnd a linear mapping that can be applied to one of these to align it optimally with the other. Dictio- naries of matched pairs of words provide the targets. Note that the dictionaries are polysemic.A word can have multiple meanings and be present in multiple pairs, so the alignment cannot be 13 one-to-one. We ﬁnd this optimal mapping between known pairs and then apply it to the entire vocabulary.

Figure 2.1: Procrustes alignment using polysemic bilingual dictionaries.

Note: Embeddings are calculated independently on each monolingual corpus. Procrustes analysis ﬁnds the rotation that best aligns term pairs in a bilingual dictionary. Terms have multiple meanings (polysemy), and can appear in multiple pairs.

This can be characterized as a matrix approximation problem: given two matrices X and Y ﬁnd a weight matrix W (a mapping from X into Y) such that ||WX −Y|| is minimized. In the general Procrustes problem, W can rotate and stretch X to align with Y. In the case where W is limited to be orthogonal, W is a rotation matrix and this loss is minimized by W = UV T , where U and

V are found through a particular singular value decomposition, YXT = UΣV T (Schonemann¨ 1966).6

6If further, the vectors in X and Y are normalized to length one, the minimization of the Euclidean distance in the loss function is identical to minimization of a cosine distance loss function. Xing et al. (2015) found this improved performance. This also makes the matrix YX(T) equivalent to a “cross-correlation” matrix between X and Y, in which case both problem and solution are very similar to those of “canonical correlation analysis” (CCA). 14

Using the Facebook MUSE implementation, we calculate this rotation iteratively with a neural net, optimizing loss in matching bilingual dictionaries, split 80/20 into training and test sets. Speciﬁcally here, we calculate monolingual parliamentary language embeddings for eight Eu- ropean languages (English, Czech, Dutch, Finnish, German, Italian, Spanish, and Swedish), and calculate the optimal alignment of each of the seven non-English embeddings to the English embeddings, aligning each based on a bilingual dictionary of 6000 word pairs. The ﬁnal result is a common embedding space with terms from all eight languages.7

2.3.3 Interpretable embeddings with decomposition and rotation

We’ve got words mapped together in a common space, but now what? We’ve indicated our target model is more like a topic model, which identiﬁes (a) a discrete number of things that a document can be characterized as containing (“topics”), (b) human interpretability of those things through parameters on words / terms that convey semantics, and (c) a measure of which, or how much, of those things any given document or group of documents actually contains. In a superﬁcial sense, topic models like LDA or STM are now not usable, since they take as inputs one number per document per term (counts, possibly weighted or regularized), rather than the multidimensional vectors for each word that an embedding model like word2vec produces. There are proposed methods for topic models with embedding inputs (Das, Zaheer and Dyer 2015), or for estimating interpretable embeddings by enforcement of sparsity (Liu et al. 2015). We argue, however, that – with an appropriate context window for the task – word2vec (and similar models like GloVe) embeddings already are a topic model, or at least they contain a hidden one.

We expose the topical structure of the embedding space through Independent Component Anal- ysis (ICA). ICA is a matrix decomposition technique that ﬁnds independent, linear transforma-

CCA has been explicitly used as a basis for many similar transfer learning problems in text analysis (Hobbs and Hopkins 2017), including other approaches to multilingual embeddings (Ammar et al. 2016; Faruqui and Dyer 2014). 7Conneau et al. (2017) extend this method to find an optimal match without the supervision of a dictionary. 15 tions of the input data. ICA can be used to estimate interpretable latent structure in data. The ideal of interpretability was defined as “simple structure” by Thurstone (1935). The specific criteria for maximally simple structure were refined and disputed over the years, but the basic idea is simple structure is obtained when observed variables have either a strong loading on a given latent factor, the closer to ±1 the better, or very small loadings, the closer to zero the better. In the extreme, observable variables are indicative of a single latent factor, and each latent factor generates the observation of particular variables but no others.

This is, in short, SVD (or PCA) followed by rotation. Rotation is standard practice, and a subject of much angst, in factor analysis but not discussed much in machine learning. The analog in machine learning / signal processing that explicitly talks about the rotation step is independent component analysis (ICA). ICA ﬁnds exactly the same subspace as PCA – by doing PCA – and then rotating to make the dimensions maximally independent, a more computationally intensive variant of simple structure.8 There are many other analogs in unsupervised learning where the rotation is baked into the identiﬁcation of the estimation, including topic models. LDA, for example, enforces an implicit “rotation” through the independence of the Dirichlet, and the simplicity of the resulting structure is further affected by the concentration hyperparameter. Systems for estimation of sparse models impose very simple structure.

The key to this working is in the matrix of associations, which should be a close analog to the association being modeled. Monroe (2017) showed, for example, that a simple “Little Jiffy” analysis of a network adjacency matrix outperformed all known community detection algorithms in benchmark networks with latent community affecting the likelihood of any given tie. Word embeddings are an already decomposed version of such a measure. It is now well-known that the word2vec procedure is approximating an arbitrarily rotated singular value decomposition of a matrix of positive pointwise mutual information (PPMI) between target words and context words (Goldberg and Levy 2014) and the GloVe embeddings go directly to this decomposition

8A less computationally intensive solution, and one more familiar to social scientists is VARIMAX rotation. 16 without utilizing neural nets. If PPMI between words across sufﬁciently large context windows is more likely to be large when those words have topical and other politically relevant semantic associations – and it is – then it should be possible to reveal that structure through decomposition and rotation.

So, to make the embeddings interpretable, We norm the embedding for any given word / token to unit length (which makes the embeddings analogous to “loadings” from factor analysis and similar models). If desirable, we reduce the dimensionality further (through a truncated singular value decomposition) and then rotate them through ICA, to positively skewed simple structure, to maximize the alignment of semantic content with (positive) dimensions of the new space.

This reveals the structures that are latent in the embeddings, including, in our case, recognizable political topics, as illustrated in Figure 2.2. Prior to rotation, there is no discernible meaning attached to any particular dimensions or locations in the embedding space. After rotation, (some of the) dimensions have clear content. In this example, there are a relatively small number of words that load relatively highly (in the tail to the right) on the horizontal dimension, including recession, economy, downturn, and inﬂation. There are a relatively small number of words that load relatively highly (in the tail to the top) on the vertical dimension, including Congo, Foreign, countries and Africa. There are a tiny number that load highly on both, including instability and crisis. There are tens of thousands with a relatively minimal positive or negative loading. It can further be noted that rotation is a linear operation, so any linear relationships present in the embeddings before rotation, as with the analogies discussed previously, should remain after rotation.

2.3.4 Document scoring

The result of the above decomposition and rotation approach is an uncovered, latent semantic space, with dimensions that map onto political and rhetorical topics of interest. The shape of 17

Figure 2.2: Rotation of embeddings to simple structure.

Unrotated SVD of embeddings Rotated Loadings, English − 50 Dimensions

Congo 0.06 Foreign 0.04 Africacountries Nigeria KenyaZimbabwe SudanBangladesh UN BurmaPakistaninternational InternationalAfrican President corruptionDRCUgandaLibya SomaliaBurmeseRwandaambassadorYemen SudanesecampcampsMuslimconflictKosovoMugabeconsular RussiaEgypt governmental peacepersecutionBosnia

0.04 peaceful 0.02

instability 0.00

0.02 crisis eurozone Monetary

Dimension 10 Dimension demandausteritybubble Component 10 depressionmonetarycurrencypressures experiencingpricesrisinggrowthcrunchtrend economicdownturn fiscalcollapse

-0.02 economics recoverywarnedeffectsunemploymentweather 0.00 factors crashpredictionsboom decline economy recession cutswarningsinflation risks ChancellorRock -0.04 −0.02

-0.06 −0.02 0.00 0.02 0.04 0.06 -0.02 0.00 0.02 0.04

Dimension 6 Component 6

Note: Rotation of embeddings reveals the (largest) semantic structures they are capturing. Be- fore rotation, the dimensions have no particular content (left). After rotation, dimensions have discernible semantics as evidenced by the high-loading words. (right). this topical space is number of words by the length of our original embeddings, 300. From here, we must ﬁnd a way to relate these word loadings to the document space. This will allow us to position not only words in a coherent, topical space, but also speeches, speakers, parties, and any other aggregation of words we might ﬁnd theoretically interesting.

Following the lead of Goldberg and Levy (2014), who express word embeddings as the decomposition of a PPMI matrix, we would also prefer our document scores to maintain a PPMI conﬁguration. A similar PPMI approach is used to translate term scores to documents by Kaimal et al. (2012). Pointwise mutual information measures the extent to which events co-occur, contingent on their baseline occurrence rate. Take two events, x and y. The PMI of these two events is:

P(x,y) PMI = log (2.1) P(x)P(y) 18

We use PPMI, or positive pointwise mutual information, because negative PMI implies that words co-occur less frequently than their baseline rates would suggest (i.e would occur solely through chance). To illustrate why positive PMI is more interepretable than negative PMI, consider the word “Canada.” Words such as ”United States”, ”Mexico”, and ”England” should have large, positive PMI. But what would a word that would reduce the expectation of co-occurence look like? This task, thinking of a word whose presence indicates that the presence of another word is less likely is difﬁcult to make sense of, which is why we, following others in the literature, use PPMI.

The ﬁrst step in producing our PPMI doc–scores is initializing each score with a prior. This is an important regularization step, as it provides an anchor or base rate for documents. For corpora containing a mix of document lengths, like the speeches analyzed here, regularization is important to prevent small documents from being overwhelmed by rare, high precision keywords. This regularization step ensures that a document score is based solely on the words that appear within the document; speciﬁcally, the extent to which those words occur at a greater than base rate expectation. This base rate expectation is calculated using the global frequency of word use throughout the corpus.

Once the PPMI has been calculated, and scores are initialized through the prior, the word scores are l2-normed to unit length, allowing the dot product to be calculated across words within a topic. This means that if words with high loadings on a topic occur more frequently than the base rate within a document, that document will receive a higher score on that topic, and vice versa for negative loadings. Note that dot products of vectors that have been normed to unit length can be rephrased as cosine similarities. The calculation of these cosine similarities introduces further regularization, because cosine similarities are calculated across the entire vocabulary, therefore adding a 0 to the cosine calculation for each word that is not present. Along with the prior, this regularization shrinks shorter documents towards base rate expectations, while there are trivial consequences for larger documents. 19

2.4 Parliamentary speech data and preprocessing

For this paper, we examine legislative speech data from eight European parliamentary lower houses: the UK House of Commons, the Spanish Congreso de los Diputados, the German Bun- destag, the Finnish Eduskunta, the Dutch Tweede Kamer the Czech Parlament, the Swedish Riksdag and the Italian Camera dei Deputati. Our corpus ostensibly covers all recorded speech, excluding utterances from the presiding Chair/Speaker, and ranges over 1989 to 2017. Speech text and speaker metadata for the Czech Republic, Germany, Spain, Finland, Netherlands, and Sweden are from the ParlSpeech data (Rauh, de Wilde and Schwalbach 2017). Speech text and speaker metadata for Italy and the UK were produced by the authors from xml and html versions of ofﬁcial records and MP biographies on parliamentary websites.9

Unique Tokens Parliament Speeches c f > 0 c f > 100 Years (cs) Czech Republic, Parlament 329,135 337,127 23,654 1993-2016 (de) Germany, Bundestag 299,844 750,866 28,180 1991-2013 (es) Spain, Congreso 290,680 533,097 21,464 1989-2015 (ﬁ) Finland, Eduskunta 245,852 1,422,492 35,767 1991-2015 (nl) Netherlands, Tweede Kamer 900,796 409,323 24,067 1994-2015 (sv) Sweden, Riksdag 317,132 638,825 21,464 1990-2015 (it) Italy , Camera dei Deputati 215,022 284,959 22,931 2006-2017 (uk/en) United Kingdom , Commons 602,763 140,717 16,674 2005-2016

Table 2.2: Corpus meta-data

The size of the data, with around a half billion tokens across corpora, informs our preprocessing strategy. We drop all words that occur in the corpus fewer than 25 times, in order to prevent the embeddings from being overwhelmed by noise and reduce the computational complexity of the rest of our operations. Unlike many other studies in political science (Denny and Spirling 2018), we keep all punctuation and stop words, do not stem or lemmatize our tokens, and keep all capitalization in the original text. We keep punctuation because it is informative – we will

9There are some inconsistencies in data generation and quality within and across these data sources. Speciﬁ- cally, the Czech corpus had been previously stripped off all punctuation. Additionally, in the UK and the Nether- lands, the list of names in roll call votes were not excluded from the corpus. 20 see, for example, that ‘?’ and ‘!’ are associated with criticism of government from opposition, and ‘£’ and ‘e’ are associated with budgetary discussion. We do not stem for two reasons. First, we are dealing with a very large corpus, and likely do not gain from aggregating across stems as much as we lose if we are unable to distinguish different meanings or usage in context. Second, there are inconsistencies with tokenization routines across languages that could introduce biases. We do not remove capitalization or stop words because both are informative, and we employ routines to prevent the high counts of stop–words from overwhelming our results.10

2.5 Validity

Validation is a critical component of any empirical study in social science (Messick 1987), but especially when applied to textual studies (Grimmer and Stewart 2013), and even more impor- tantly when applied to unsupervised models (Slapin and Proksch 2008), like the one described above. While we expect the model to produce some novel insights, simply due to the amount of information processed, it should also produce results consistent with existing theory and scholars’ common understanding of European politics. When possible, we borrow tests used in validating other embedding approaches, to ensure comparability.

2.5.1 Content validity of cross-lingual topics

Our ﬁrst objective is to identify substantive political topics, with content that is linguistically and politically consistent across our multiple languages and parliamentary settings. Table 2.3 lists the 45 substantive topics that appear to meet this criterion. Some of these topics represent

10Tokenization was done using the language speciﬁc modules provided by SpaCy. Embeddings were trained using the original word2vec C implementation, wrapped in Python through the gensim module. Procrustes analysis was GPU accelerated using CUDA version 9.0, and ICA was done through the scikitlearn implementation of the FastICA algorithm. All of these steps of the analysis were conducted on Microsoft Azure virtual machines. Further processing, including document and party scoring was conducted with local computing resources in Python and R. 21

Ministries that essentially all democracies have, such as Health, Education, and Transporta- tion. Ministries that have a wider policy ambit are split between multiple topics. For example, Business, Banks, and Macroeconomy all touch on separate, but related issues that would fall under the Finance Ministry. Similarly, issues that would fall under the purview of the Labor Ministry are split into three topics: Workers, which focuses on labor rights and protections; Professions, which deals with the particular issues affecting qualified professionals; and Jobs, which discusses unemployment and work prospects. A similar configuration occurs with environmental issues: the Environment topic focuses more narrowly on the natural environment, conservation, and pollution; Energy deals with renewables and debates over fossil fuels; Agri- culture focuses on particular environmental issues insofar as they intersect with agriculture; and Global (Aid/Climate) deals with international summits and transnational institutions. There are also a series of thematically related and politically contested issues that do not fit neatly into ministerial categories: these include Immigration, Discrimination, Bureaucracy, and Religion. There do not seem to be any particular holes present in the data. There is representation of social issues, economic issues, and no clear biases towards a particular part of the political spectrum.

We ﬁrst note that, as is no longer much of a surprise from topic models, the token lists are evocative, tending to cohere well into related concepts conveyed almost entirely by nouns. Even those that may seem out of place to many – like Rock in Banking – are more obviously related when familiar with the context: Northern Rock was a bank that failed in the banking crisis, was bailed out, and was ultimately taken into public ownership, all a subject for extensive parliamentary debate related to ... banking. The top token for Global (Aid/Climate) is .7, refer to the .7% of GDP that countries have pledged to devote to international development through the Millenium Devlopment Goals. Perhaps more to our point here, the terms cohere cross-lingually. The Im- migration topic, for example, contains variants of words for immigrant, migrants, asylum and other related concepts across all languages.

We note, as a further validity check, that most of the tokens on these key lists are not in the 22

Table 2.3: Politically substantive topics

Topics Top English keywords Terrorism terrorism; terrorist; threat; intelligence; terrorists Representation election; elections; electorate; elected; voters Professions professional; qualified; profession; trained; nurses Inequality poorest; inequality; rich; fairness; society Discrimination men; discrimination; equality; women; female Health doctor; medical; hospital; NHS; doctors Agriculture farmers; agricultural; farming; agriculture Agreements agreement; agreements; negotiations; negotiate; cross Environment environment; Natural; wildlife; biodiversity; beauty International Crises Zimbabwe; Iran; Foreign; Burma; Sudan Private/Public private; public; sector; privatisation; voluntary Banks Rock; shareholders; company; RBS; assets Families family; mother; father; wife; mothers Public Health smoking; alcohol; substances; tobacco; products Budget money; resources; funds; expenditure; budget Energy energy; electricity; renewables; Energy; Ofgem Global (Aid/Climate) 0.7; climate; G8; DFID; International Macroeconomy deficit; fiscal; economy; borrowing; finances Media television; broadcast; media; BBC; broadcasting Courts court; courts; judicial; tribunal; appeals Jobs unemployed; jobseeker; incapacity; unemployment; Work Rights liberties; freedom; liberty; freedoms; rights Prisons prison; prisons; prisoners; custody; Prison Maritime sea; fishermen; fishing; vessels; maritime Local/Regional counties; constituency; regional; cities; areas Religion Muslim; religious; gay; religion; Christian Bureaucracy bureaucracy; tape; administrative; bureaucratic; savings Transport Transport; railways; transport; rail; roads Business businesses; sized; tape; small; enterprises Education school; teachers; schools; teacher; Education Housing housing; houses; Housing; house; homes Crime offence; crime; offences; crimes; fraud Science/R&D science; investment; technology; R Sport sport; Olympics; sports; sporting; games Taxes taxation; tax; HMRC; taxes; Paymaster Labor workers; employees; employer; employers; staff Defense Army; Defence; defence; troops; reservists Disabilities disabilities; disabled; disability; Disability; DLA OECD/Trade Germany; France; Sweden; Canada; Australia Immigration immigration; asylum; migrants; Immigration; migration Europe European; Europe; Brussels; EU; directive History/Heritage museum; museums; anniversary; memorial; heritage Universities university; universities; student; students; fees

Note: This table contains the top ﬁve English keywords for the politically substantive topics extracted by our model. Longer keyword lists in all languages can be found in the Appendix. 23

Table 2.4: Rhetorical topics

Topics Top English keywords Issues issues; matters; circumstances; questions; situations Nonsense nonsense; misleading; somehow; neither; utterly Groups Association; Society; Federation; organisations; Institute Compliance fines; inspection; penalties; CQC; sanctions Failure promises; failed; promised; promise; Speech Studies analysis; study; survey; evaluation; commissioned Decisions decisions; decision; steps; account; choices Consequences consequences; impact; unintended; effect; effects Statistics trend; numbers; figures; statistics; number I Am glad; afraid; sorry; grateful; pleased Objectives objectives; objective; achieve; achieving; targets Standards level; threshold; high; levels; inflation Initiatives pilot; awareness; engagement; learn; initiatives Reasons reasons; reason; explain; why; arguments Timetable timetable; delay; wait; summer; recess Disaster disaster; tragic; tragedy; incident; died Quotes read; Times; Daily; newspaper; article Problems/Solutions problems; solve; problem; solution; solutions Comparisons rather; Rather; More; less; more Skepticism might; somewhat; bit; seem; seems Wondering wonder; explain; how; decide; whether Questions/Answers answer; questions; answers; question; answered Praise tribute; pleasure; thanks; gratitude; praise Differences difference; distinction; between; differences; relationship Alternatives options; option; alternative; alternatives; choice Costs costs; cost; taxpayer; incurred; expensive Uncertainty uncertainty; concern; fears; risk; danger Deliberations debate; debates; pleasure; discussion; meetings Change change; changes; changing; changed; radical My Friend; Friends; constituency; constituent; constituents Rules rules; guidelines; guidance; law; definition Procurement procurement; contract; value; project; contractors Transparency information; transparency; openness; disclosure; Information

Note: This table contains the top ﬁve English keywords for rhetorical topics extracted by our model. Longer keyword lists in all languages can be found in the Appendix. 24 bilingual dictionaries, which are dominated by more commonly used words. To provide an easy example, the dictionaries have no proper nouns or punctuation marks, so their appearance together requires that in multiple languages they are in a similar location with respect to the words that do have dictionary pairs.

In addition to substantive topics, the model also uncovered politically interesting dimensions that are rhetorical in nature. These 33 topics, and five of their highest English loading keywords, are listed in Table 2.4. These reflect specific patterns in how legislators speak, and politically interesting commonalities in how arguments are made. Certain topics express disagreement and are more associated with opposition parties: these include #Uncertainty, #Skepticism, #Nonsense, and #Failure. Others reflect deliberative strategies, for example Studies or Reason. Two topics are devoted to personal references, #I Am, which denotes MPs appeals to their own emotions, and #My, which denotes personal references to friends, constituents, etc.

On the face of it, the content of the topics the model derived seem valid. There are a wide range of substantive topics with sensible keywords that seem to cohere semantically within topics and distinguish across topics. The rhetorical topics reﬂect clear patterns in rhetoric and argumentation. There is a mix of languages within each topic that seem to arise from similarly structured embedding spaces and are not artifacts of the dictionaries used in tying those spaces together.

There seems to be strong content validity amongst the tokens themselves and their organization into topics. But do these results maintain their validity when aggregated at politically meaningful levels? To demonstrate this, we will use four topics as running examples — Business, Inequality, Energy, and #Failure.

Figure 2.3 shows topic attention over time for Energy. These ﬁgures show relative attention over time, within country, so that each country is centered around zero for the entire time period covered by the corpus. Unsurprisingly, the Green parties dominate this topic when they are 25

Energy Scores, United Kingdom Energy Scores, Sweden 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Energy Scores, Netherlands Energy Scores, Italy 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Energy Scores, Finland Energy Scores, Spain 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Energy Scores, Germany Energy Scores, Czech Republic 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Figure 2.3: Attention to Energy over time 26

Inequality Scores, United Kingdom Inequality Scores, Sweden 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Inequality Scores, Netherlands Inequality Scores, Italy 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Inequality Scores, Finland Inequality Scores, Spain 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Inequality Scores, Germany Inequality Scores, Czech Republic 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Figure 2.4: Attention to Inequality over time 27

Business Scores, United Kingdom Business Scores, Sweden 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Business Scores, Netherlands Business Scores, Italy 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Business Scores, Finland Business Scores, Spain 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Business Scores, Germany Business Scores, Czech Republic 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Figure 2.5: Attention to Business over time 28

#Failure Scores, United Kingdom #Failure Scores, Sweden 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

#Failure Scores, Netherlands #Failure Scores, Italy 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

#Failure Scores, Finland #Failure Scores, Spain 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

#Failure Scores, Germany #Failure Scores, Czech Republic 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Figure 2.6: Attention to #Failure over time 29 seated in parliament. In situations where there are no explicit Green parties seated, such as in Spain, the regional party most committed to environmental issues, ERC, devotes disproportionate amount of time to discussing Energy, along with the leftist party IU. In Italy, which only had a Green party seated at the beginning of the dataset, the environmentally focused, but politically heterodox, Five Star Movement (M5S) dominates discussion of this topic.

Relative attention over time for Inequality is shown in Figure 2.4. In countries where they are seated, Leftist parties devote a disproportionate amount of attention to this topic. The primary exception to this is the Czech Republic, where the former ruling Communist party is considered less economically left–wing than its Communist origins might suggest. The greatest discrepancy between Leftists and other parties occurs in Germany following the grand coalition produced by the 2005 election, which seems to dramatically reduce the Social Democratic SPD’s attention to Inequality.

On the other end of the traditional left vs. right economic cleavage, Figure 2.5 shows discussion of Business over time. In contrast with Inequality, this topic is dominated by Conservative parties. The main exception to this is Sweden, where discussions of Business are dominated by the nominally Agrarian Centre party, which has shifted recently to a more liberal orientation and a focus on free markets. In Spain, the topic is dominated by the right–wing, Christian– Democratic/Regional party PNV. In Italy, Lega Nord is often neck and neck with Burlesconi’s Forza Italia, reﬂecting the LN’s regional support in the more economically developed North, and its concomitant focus on free market, pro–business policies.

Finally, 2.6 departs from the previous topics, which had a clear relationship with a particular party family to a rhetorical topic, #Failure. This topic clearly separates governing and opposition parties, with no governing parties scoring above the country average, excluding a few years in Finland. This is unsurprising; it is incumbent upon the opposition to point out and criticize the failures of the governing party, who are loathe to discuss anything that might imply obstacles to their program. Instead, when they are forced to use negative rhetorical topics, governing parties 30 are more likely to use less harsh frames, such as #Change or #Differences.

2.5.2 Validity of linear political semantics

Perhaps the most striking feature of word embeddings is their ability to characterize meaningful semantic relations, like is the capital of or is the male counterpart to by linear differences between embedding vectors. This is demonstrated in this literature by completing analogies through vector math. If we have in fact modeled meaningful associations between language and political content, and maintained linearity, we might hope that our vector space has similar properties, capturing politically meaningful relationships. It does seem to do so, which we demonstrate with a new semantic party analogies task.

Borrowing from the embeddings literature, we undertake two tests of the validity of the “political semantics” of our model. While the party systems under study vary considerably from one another, there are many basic similarities and differences: the Conservatives in the UK are similar to the CDU in Germany; and the Vansterpartiet¨ in Sweden is to the left of the Socialists in Sweden much like Die Linke is to the left of the SPD in Germany. To test whether our model can capture these relationships, we devise party analogies. These analogies are simple vector math. We take the vector for a party in a certain country, subtract from it a different party with a different ideological orientation, then add the vector for a party from a different country with the same ideological orientation. If the topic space is truly reﬂective of underlying political reality, this vector math should choose the corresponding ideological orientation with a new country. This produces the analogies or pairs shown in Table 2.5.

First, in Table 2.5, we measure whether or not the model can detect the classic distinction between centre-right and centre-left parties, as represented by the Conservatives and Labour in the UK. For each additional country, the closest analogous pair also represents the main centre-right and centre-left parties. The two countries where it guesses the correct answer with the least 31

Table 2.5: Semantic party analogies

uk Con (Conservative) is to uk Lab (Labour) as is to Cosine Expected de CDU/CSU (Christian Dem.) de SPD (Social Democratic) 0.60 de SPD fi Kok (National Coalition) fi SDP (Social Democratic) 0.59 fi SDP nl CDA (Christian Democratic) nl PvdA (Workers’) 0.57 nl PvdA sv M (Moderate) sv SAP (Social Democratic) 0.49 sv SAP es PP (People’s) es PSOE (Socialist Workers’) 0.38 es PSOE it Fi/PdL (Pole of Freedoms) it PD (Democratic Party) 0.27 it PD cs ODS (Civic Democratic) cs CSSD (Social Democratic) 0.23 cs CSSD fi SDP (Social Democratic) is to fi Vas (Left Alliance) as is to Cosine Expected es PSOE (Socialist Workers’) es IU (United Left) 0.63 es IU sv S (Social Democratic) sv V (Left) 0.49 sv V cs CSSD (Social Democratic) cs KSCM (Communist Party) 0.48 cs KSCM nl PvdA (Workers’) nl GL (Green Left) 0.46 nl SP it PD (Democratic Party) it SEL (Left Ecology Freedom) 0.30 it SEL de SPD (Social Democratic) de PDS/LINKE (The Left) 0.26 de PDS/L No left party target: uk Lab (Labour) uk Green 0.28 None de SPD (Social Democratic) is to de GRUENE (Green) as is to Cosine Expected it PD (Democratic Party) it FdV (Fed. of the Greens) 0.68 it FdV sv S (Social Democratic) sv MP (Green) 0.50 sv MP uk Lab (Labour) uk Green 0.46 uk Green fi SDP (Social Democratic) fi KD (Christian Democrats) 0.24 fi Vihr nl PvdA (Workers’) nl GL (Green Left) 0.13 nl GL No green party target: es PSOE (Socialist Workers’) es ERC (Rep Left of Catalonia) 0.36 None cs CSSD (Social Democratic) cs Usvit (Dawn) 0.34 None fi Vas (Left Alliance) is to fi PS (True Finns) as is to Cosine Expected cs KSCM (Communist Party) cs SPR-RSC (Rally for the Republic) 0.37 cs SPR-RSC uk Plaid (Plaid Cymru) uk UKIP (Independence Party) 0.23 uk UKIP sv V (Left) sv NyD (New Democracy) 0.22 sv NyD nl GL (Green Left) nl LPF (Pim Fortuyn) 0.22 nl LPF it SEL (Left Ecology Freedom) it M5S (Five Star) 0.17 it LN No nationalist party target: es IU (United Left) es CDS (Democratic and Social Centre) 0.19 None de Linke (Left) de Gruene (Green) 0.16 None

Note: The analogy A is to B as C is to ? is completed by calculating the party, from those in the same country as C, with the vector closest, by cosine similarity to VB −VA +VC. The expected analogous party is listed to the right. A bolded entry indicates a match with expected relationship. An italicized entry indicates a clear mismatch. 32 precision are Italy and the Czech Republic, which have the most unstable party systems in our data.

Second, we measure whether or not the model is able to effectively discriminate between the more ﬁne pairing of social democratic and leftist parties. Again, we see that the model performs admirably at this task, effectively discriminating between the centre- and far-left. In the case of the UK, which does not have a clear leftist party, the model chose the Greens as the closest comparison. The only miss among the analogies is the Netherlands, where the model selects the Green Left, a party formed from the merger of other left–wing parties, including the former Communist Party, making the GL one of the more leftist Green parties.

Third, we look at the relationships between centre-left, typiﬁed here by the SPD in Germany, and Green parties, using the Germany Gruene party. Again, the model makes mostly correct predictions, with the exception of incorrectly suggesting the green party in Finland. Finally, the last analogy task reaches across the political spectrum, comparing the Finnish Left Alliance with the radical right True Finns party. Again, there is a single incorrect analogy, with the populist Five Star Movement incorrectly predicted as radical–right.

The model’s success on the party analogies illustrates the valuable opportunity offered by our approach. Our overarching goal is to create a common geometric space among different countries and languages, where not only words, but documents, parties, speakers, or any other unit of aggregation that have the same meaning will have the same relative position. The semantic party analogy task is the greatest sign that this space is, indeed, valid. 33

2.6 Common Space Understandings of Political Competition

2.6.1 What is the shared landscape of interparty competition?

We believe then that our method recovers a common, politically relevant space across multiple languages, that documents and aggregates of documents can be located in that space, that if two documents are close to each other, they are likely to have similar semantic content, and that if they are distant from each other, their relative location may imply particular political semantics. This implies further that we may be able to “scale” parties, across linguistic contexts, in a politically and perhaps ideologically meaningful way, a longstanding objective in comparative politics generally and speciﬁcally among comparative text as data scholars (Slapin and Proksch 2008; Laver, Benoit and Garry 2003; Lowe et al. 2011a).

Extracting that shared political semantics means we seek an even lower dimensional representation of our substantive topic space. If we take that meaning to be ideological, we are able to find it because ideology is reflected by parties emphasizing particular topics over others. In other words, ideology would be a set of factors which best explain the underlying variance in the specific issue areas (conflict, economic, immigration, etc.) under discussion. To find this, we return to the singular value decomposition, applying it to the the party topic scores.11

Figure 2.7 shows this scaling. The size of each point is proportional to the number of speeches a party makes, and the arrows represent the topic loadings drawn from the V matrix of the SVD. The x–axis represents a traditional left–right economic split. Negative values are associated with free-market, conservative ideology, with Christian Democratic, Liberals, and Conservative parties all scoring highly along this dimension. On the positive side, left–wing parties represent the extreme end of the spectrum, closely followed by Social Democratic parties. This axis is deﬁned topically with Business loading highest on the right–wing side, and Inequality / Work-

11Because these vectors may have very different sampling variance due to variations in length, We weight the observations, with weights proportional to the square root of the total number of terms used by the party. 34

Weighted SVD of Mean Party Scores

0.3 sv_C Energy ● Ecological ● Left Agriculture ● Social Dem. Local/Regional ● fi_Kesk Liberal Environment ● Christian Dem. nl_PvdD ● Conservative it_M5S ● Nationalist 0.2 ● Agrarian it_FdV sv_MP ● Ethnic/Regional Agreements cs_CMUS Courts/Constitutional Telecom it_PD fi_Vihr Budget Agencies/Bodies es_CDS 0.1 Science/R&Dit_PT sv_SAP cs_ODA fi_LKP Jobs it_IdV nl_CDA uk_Green it_FI/PdL Transport de_Gruene Private/Public it_SEL es_PNV cs_KDS uk_SNPnl_PvdA nl_VVDcs_ODS uk_LibDem it_FLI es_ERC GlobalDiscrimination (Aid/Climate) uk_SDLP it_UdC fi_PS de_SPDDisabilities nl_GL

Terrorism Taxes cs_VV Banks cs_ANOes_PPnl_SGP nl_D66 cs_CSSD de_CDU/CSU Representation 0.0 de_Linke es_CiUnl_CUUniversitiesuk_UKIP sv_V sv_M uk_Plaid

cs_KDU−CSLDefense it_SCit_LNSport fi_Nuor it_P−UDEUR Inequality cs_US es_PSOE Housing fi_RKP it_MDP cs_KSCM es_IU Maritime cs_TOP09 nl_SP Business Macroeconomyde_FDP uk_Lab History/Heritage it_PRC fi_Vas fi_Kok cs_SPR−RSC Prisons sv_KD fi_SDP Europe it_AN/FdIOECD/Trade uk_Con uk_DUP sv_LEducationes_UPyD it_PdCI cs_LIDEM −0.1

Nationalism ~ Environmentalism BureaucracyCrime Public Health Workers it_DCA−NPSI Rights Media Professionsit_PI/DES−CD cs_Usvit it_AP/NCD sv_SD it_RnP nl_LPF Religion nl_PVV sv_NyD fi_KD −0.2 FamiliesInternational Crises ImmigrationHealth −0.3

−0.3 −0.2 −0.1 0.0 0.1 0.2 0.3

Right ~ Left

Figure 2.7: Dimensions of weighted SVD of topic space by party

Note: X–axis captures traditional left–right economic splits. Y–axis largely captures environmentalism vs. nationalism. 35 ers loading highest on the left–wing side. The y–axis maps onto a “New Politics” dimension, with positive values indicating a focus on the environment, and negative values associated with nationalism. The former is, unsurprisingly, associated with Green and Agricultural parties with the nationalist side most closely associated with the radical right and Christian Democrats.

2.6.2 Can this improve existing understandings of party families?

Party families are an enduring and influential concept in the study of European politics (Mair and Mudde 1998a). One widely used classification of parties by family is found in the Compar- ative Manifesto Project (Merz, Regel and Lewandowski 2016). CMP defines the following ten party families that appear in our data: Ecological (ECO); Left (LEF); Socialist and Social–De mocratic (SOC); Liberal (LIB); Christian–Democratic (CHR); Conservative (CON); Nationalist (NAT); Agrarian (AGR); Ethnic and regional (ETH); and Special Issue Parties (SIP). However, as with any categorical measurement, significant controversy exists over the extent to which party families are coherent units, as well as conceptual difficulty with parties who drift into new families over time, merge, split, or coalesce in different alliances, lists, and groups for elections and parliaments at different times and levels.12 We believe our measurement has considerable flexibility to address these issues, drawing on more comprehensive data (plenary speeches as opposed to manifestos) and ability to track changes in party orientation dynamically.

Figure 2.8 is complex but, we hope, rewards inspection. Each political party is represented by a row of dots, arrayed horizontally based on the cosine similarity between that party’s vector across the substantive topic space and the average vector for each party family, as deﬁned by CMP. For example, the top line in the graph shows the Swedish Green party, which has the highest cosine similarity with the ecological party family average, followed by the Leftist family average, and is most dissimilar to the Conservative party family average.

12This difficulty is extreme in the case of Italy. Our data reflect dynamic, fluid, and often small “parliamentary groups,” rather than the broad alliances that have contested national elections and offered manifestos in recent years. 36

Cosine Similarities with Party Family Means

● ● ●● ● ● ● ● sv_MP ● ● ● ●●● ● ● it_FdV ● ● ● ●● ● ● fi_Vihr ● ● ● ● ● ● ● ● uk_Green ●●● ● ● ● ● ● nl_PvdD ●● ● ● ●● ● ● de_Gruene ● ● ●●●● ● ● sv_C ● ●● ● ● ●● cs_CMUS ● ● ● ● ● ● ● ● sv_V ● ●●● ● ● ● ● es_IU ● ●● ● ● ● ● ● it_PRC ● ● ● ●● ●● ● it_PdCI ● ● ● ●● ● ● ● de_Linke ● ● ●● ● ● ● it_SEL Party Family ●●●●● ● ● ● fi_Vas ● ● ● ●● ● ● ● nl_SP ● Ecological ● ● ● ● ●● ● ● cs_KSCM ● ● ● ●● ● ● ● nl_GL ● Left ●● ● ● ● ● ● ● it_RnP ● Social democratic ● ● ●●● ●●● it_P−UDEUR ● ● ● ● ●● ● ● ● uk_Lab Liberal ● ● ●● ● ● ● ● fi_SDP ● Christian ●●● ●● ● ● ● de_SPD ● ●●● ● ● ● ● sv_SAP ● Conservative ● ●● ● ● ● ● ● nl_PvdA ● ● ●●● ● ● ● it_MDP ● Nationalist ● ● ●● ● ● ● ● es_PSOE ● ●● ●● ● ● ● cs_CSSD Ethnic/Regional ●● ●● ● ●● ● it_PD ● ● ●● ● ● ● ● it_M5S ● ● ● ● ●● ● ● fi_LKP ● ● ● ●● ● ● it_IdV ● ● ● ● ● ● ● ● es_UPyD ●● ● ●● ● ● ● uk_UKIP ● ● ● ●● ● ● ● nl_D66 ●● ●●● ● ● ● cs_VV ● ● ● ● ● ● ● uk_LibDem ● ● ● ● ● ● ● ● cs_US ● ● ● ●●● ● ● fi_Nuor ● ● ● ● ●● ● ● es_CiU ●● ●● ● ● ● it_AP/NCD ● ● ● ● ●● ● sv_KD ● ● ● ● ● ● ● ● it_PI/DES−CD ● ●●● ●● ● ● nl_SGP ● ● ●● ● ● ● ● cs_KDU−CSL ● ● ● ●● ● ● ● fi_KD ●● ● ●● ● ● nl_CU ● ●● ● ● ●● ● it_UdC ● ● ●●● ● ● ● cs_TOP09 ● ● ● ● ● ● ●● fi_Kesk ●●● ● ● ● ● cs_KDS ● ● ●●● ● sv_L ● ● ● ● ● ● ● ● es_PP ● ● ● ●● ● ● ● nl_CDA ● ●● ● ● ● ● ● nl_VVD ● ● ●● ● ● ● ● uk_Con ● ● ● ● ● ● ● it_FI/PdL ● ● ● ●● ● ● ● sv_M ● ● ● ● ●● ● ● fi_Kok ● ●●● ●●● ● cs_ODS ● ● ● ●● ● ● ● it_SC ● ● ●● ● ● ● ● it_FLI ●●●● ● ● ● ● it_PT ● ● ● ●● ● ● de_CDU/CSU ●● ● ●●● ● ● de_FDP ● ●●● ● ● ● cs_ANO ● ● ● ●●● ● ● es_CDS ● ● ●●●● ● uk_SNP ●●● ● ● ●● ● nl_PVV ● ● ● ●●● ● ● nl_LPF ●● ● ● ●● ● ● sv_SD ●●● ● ● ● ● ● sv_NyD ● ● ● ●● ● ● it_AN/FdI ● ● ●● ●● ● ● it_LN ● ● ● ● ●● ● fi_PS ●● ● ● ● ● ● cs_SPR−RSC ●● ● ● ● ● ● ● cs_Usvit ●● ● ●● ● ● ● cs_LIDEM ●●●● ● ● ● ● it_DCA−NPSI ● ●● ●● ● ● uk_SDLP ● ● ● ● ● ● ● ● uk_DUP ● ● ● ● ●● ● ● es_PNV ● ● ● ●● ● ● ● fi_RKP ● ● ● ●●● ● ● cs_ODA ● ● ● ● ● ● ●● es_ERC ● ● ●●● ● ● uk_Plaid

−1.0 −0.5 0.0 0.5 1.0

Figure 2.8: Assessing the ﬁdelity and clarity of party family memberships

Note: Cosine similarities between each party vector and each party-family-mean vector, deﬁned by Comparative Manifesto Project party family codes. The color on the right next to party label indicates the original CMP family. The parties are grouped by highest similarity family, except ”Special Issue” and ”Agrarian”.) 37

On average each party falls closest to its party family, as deﬁned by CMP. However, there important divergences between our measure and CMP. For example, Green Left (GL) in the Nether- lands is coded as an Ecological party, but is closest to the Left by our measure, not a surprise since it was formed as a merger between the Communist party and other radical leftist parties. In Germany, the ruling CDU is closer to the Conservatives and Liberals than their Christian– Democratic brethren. Similarly, the CDA in the Netherlands is an ostensibly Christian party that has adopted an increasingly centrist track as a frequent member of the ruling coalition. The Cen- tre Party in Finland is formally an agrarian party13, but, as their name implies, positions itself to the ideological center, capture in our dataset by being closest to both Christian–Democrats and Conservatives.

Another feature of our approach is our ability to detect the ideological positions of parties such as special interest parties or ethnic and regional parties, whose family coding does not have a clear ideological dimension. The New Democracy party in Sweden (NYD), has the closest cosine similarity with the Nationalist party family, which corresponds to its anti–immigrant messaging. The Reformed Political Party in the Netherlands (SGP) is a fundamentalist, Calvinist party, and is closet to the Christian–Democratic family in our model. The animal rights party in the Netherlands (PvdD) is closest to the Green party family. The Union of Democrats for Europe (P-UDEUR), which achieved its greatest electoral success as part of a centre–left coalition, is closest to the Social–Democrats.

To provide a different view of differences between our closest party family and the groups de- ﬁned by CMP, Table 2.6 presents a confusion matrix between the two party groupings, with the original party in the CMP coding on the vertical axis, and the closest party family mean vector on the horizontal axis. Again, this is accomplished without any a priori information about party families. Not surprisingly, most of the parties in the off–diagonal elements are smaller parties that have had little representation in parliament. These are, of course, most prone to miscoding,

13Although they are technically a member of the Liberal parties, Finland’s Liberals differ from their European counterparts in emphasizing decentralization, rather than laissez–faire economics. 38

Table 2.6: Consistency of Manifesto Project Party Families in Vector Space

Closest Mean Party Family Vector CMP ECO LEF SOC LIB CHR CON NAT ETH Party Family Ecological 5 parties nl GL Left 10 parties Social Dem. 9 parties de FDP cs ANO Liberal cs CMUS 8 parties sv L cs ODA nl VVD it SC nl CDA Christian Dem. 10 parties es CDS de CDU Conservative 8 parties Nationalist uk UKIP 10 parties cs VV Ethnic / Regional uk SNP 6 parties es CiU Special Issue nl PvdD it P-UDEUR it M5S nl SGP sv NYD it AP/NCD it PT Not in CMP data it MDP it DCA-NPSI it PI/DES-CD it FLI

Note: A party’s presence in a particular cell indicates its party family as coded by the Compar- ative Manifesto Project (rows) with the closest party family in our vector space, deﬁned by the mean of the party vectors (columns). as a party might not convey enough information in small speeches to gain a coherent picture of their ideology. The model shows strong performance, missing no left–wing or Social Demo- cratic parties, with most of the other misses likely due to the incongruity of the CMP families with parties recent ideological stances (e.g. the CDU in Germany and CDA in The Netherlands). Perhaps most impressively, the model correctly guesses six out of nine ethnic or regional parties, which are quite ideologically heterogeneous and seat only a small number of MPs.

How does the semantic space we revealed do so well at accurately identifying party families? The answer lies in the fact that the various emphases parties place on topics are highly speciﬁc and distinct within party families, particularly from those parties which are unburdened by governing, and whose speech is therefore more indicative of their ideology. To demonstrate this, we rotate the SVD of the topical space shown in Figure 2.7 using VARIMAX. As the reader will recall from the methodology section, this entails aligning points along the dimensions while maintaining the underlying relationships in the data, in order to produce a “simple structure.” 39

Left dimension

it_PRC it_RnP it_SEL cs_KSCM fi_Vas sv_V es_IU it_PdCI de_Linke nl_SP

−0.3 −0.2 −0.1 0.0 0.1 0.2

Nationalist dimension

sv_SD fi_PS cs_SPR−RSC uk_UKIP nl_PVV cs_Usvit it_LN sv_NyD nl_LPF it_AN/FdI cs_LIDEM

−0.4 −0.3 −0.2 −0.1 0.0 0.1 0.2

Green dimension

fi_Vihr nl_GL sv_MP it_FdV nl_PvdD uk_Green de_Gruene

−0.4 −0.3 −0.2 −0.1 0.0 0.1 0.2

Agrarian dimension

fi_Kesk sv_C

−0.4 −0.3 −0.2 −0.1 0.0 0.1 0.2

Christian dimension

fi_KD es_CDS cs_KDU−CSL nl_CDA it_UdC sv_KD it_PI/DES−CDit_AP/NCD nl_CU cs_TOP09 cs_KDS it_DCA−NPSI nl_SGP de_CDU/CSU

−0.3 −0.2 −0.1 0.0 0.1 0.2

Figure 2.9: Varimax rotation of substantive and rhetorical topics 40

Figure 2.9 illustrates this simple structure for ﬁve dimensions. These dimensions clearly map onto the niche party families present in our data, with the understandable exception of ethnic / regional parties. To restate, this result is solely based on matrix operations performed on the original word x topic space, without any external information on party families or ideologies.

2.7 Conclusion

This paper proposed a new framework for evaluating political text in multiple languages by training embeddings on separate corpora, projecting those embeddings on to a shared space, decomposing this shared space into topic vectors, and then weighting these to create a document by topic space. We show the validity of our method, both on its face, in response to external events, in correspondence with theoretical understanding, and in its ability to correctly solve semantic party analogies. After validating our model, we show its application to difﬁcult political science questions like scaling party ideology cross–nationally, and binning parties into discrete families.

One of the obvious next steps of this project is to get more data on other languages and countries. While working on this paper, we found that as we increased the number of languages, the quality of our substantive topics increased, likely due to reducing the inﬂuence of country or language idiosyncrasies. Beyond this, we believe this method will prove similarly successful in multilingual contexts outside European politics. This could include analyses of social media data, alternative forms of political communication such as non–Parliamentary speeches or manifestos, multilingual surveys, or media coverage across multiple countries.

Two additional avenues to refine our model also stand out. In order to place the embeddings in a common space, we use a Procrustes approach, rotating the different language models onto the original English space. An alternative could be to use CCA, which finds a shared space or bridge between both original matrices. An even more radical step could be to avoid the use of 41 word embeddings entirely. We chose to use embeddings initially both for computational reasons and to not depart too dramatically from the existing literature on multilingual NLP. But we are, in effect, doing nested SVDs which might not be strictly necessary. Instead, we could begin by first decomposing the constituent languages into topic spaces. Then we could align these spaces with one another directly. We could also attempt to align established topic models like LDA or STM directly, but we expect that nonlinearities would cascade and lose the shared linear semantic structure that seems to be an important factor in the method’s success.

Another area that will be fruitful for future research is in understanding the various statistical properties of multiple languages, especially how morphological or semantic differences create artifacts in the topic space. For example, Finnish and German are languages with compound nouns. This is why keywords from these languages dominate the top keyword lists: compound nouns are almost by deﬁnition higher precision words, e.g. Menschenrechtsverletzungen, a Ger- man word meaning “human rights violations” in English. Additionally, many dimensions are spent absorbing language idiosyncrasies, providing “excess topics” consisting of particular verb conjugations, for example. Better understanding these properties could greatly improve the gen- eralizabiity of the model’s results. 42

Chapter 3

The Radical Right in Parliament: Anti–immigration, Authoritarianism, and Populism

3.1 Introduction

In 1946, a group of Mussolini’s Fascist supports established the Italian Social Movement, the ﬁrst post–war party in a transnational political movement that would later be called the radical right.1 Beginning with a “third wave” of nationalist parties winning electoral victories in the late 80s and 90s, and the potential catastrophic consequences that their ideology portended, scholarship on this particular family has exploded in recent decades. This paper contributes to this growing literature by focusing on parliamentary competition within the radical right’s core ideological precepts: anti–immigration, authoritarianism, and populism. Drawing on new data

1There has been considerable discussion for an adequate name for this party family characterized by ties to fascist organizations, anti–immigrant and euroskeptic policy positions, and populist rhetorical strategies. While I prefer the term radical right as most conceptually accurate, throughout this paper I will use the descriptor nationalist to maintain consistency with the Comparative Manifestos Project. 43 and a new methodological approach, I show how nationalist parties adapt their exclusionary ideology to the legislative environment. This paper demonstrates that, with few exceptions, the radical–right does not have uncontested ownership over its core issues, with mainstream conservative parties frequently taking a similar, but less extreme, line. I also show that other right–wing niche competitors adopt adversarial responses. Additionally, I ﬁnd that while both left–wing and right–wing populists adopt an anti–establishment position, there are clear differences between the spatial conﬁguration of populist discourse among leftist and nationalist party families.

The paper proceeds as follows. First, I offer a brief review of the literature on nationalist parties, highlighting their three main issue areas of immigration, authoritarianism, and populism. I then develop a new methodological approach where the corpus–level topics discovered in the ﬁrst chapter can be adapted to provide issue area–speciﬁc insights. I then turn to the three main issue areas in turn. For immigration, I discuss two topics: Immigration and Religion. For authoritarianism, I focus on how parties compete within the Crime and Public Health topics. For populism, I move beyond intra–right–wing competition to contrast the rhetorical strategies of left–wing and right–wing populists and their position vis–a–vis the European Union. I conclude with directions for further research.

A note for readers. Throughout this chapter I will refer to ideological competiion and fragmen- tation across a variety of different aggregations. At the highest conceptual level, we have the pillars of far–right ideology: anti–immigration; authoritarianism; and populism. Competition along these lines is delineated through the substantive topics that were discussed in Chapter 2. Since these topics are rather broad and focus on all political competition, this chapter is further derived into sub–topics, sometimes referred to as frames, through which competition within each parent topic identiﬁed in Chapter2 is mediated. 44

Charity Families Violence Immigration Religion Democracy Christianity Schools Gender Anti–Immigration Applications Terroirsm Economic Impact Vulnerable Groups Immigration IDP EU Asylum Workers Courts Prison Victims Police Crime Rights Offenses Local Drugs Authoritarianism Weapons Crime Consumers Animals Public Health Smoking Gambling Alcohol Children Markets Sovereignty Eurozone NATO Europe Threats Populism EU Members Others New Members Failure – (rhetorical) Nonsense – (rhetorical) Representation – (rhetorical)

Table 3.1: Ideological pillars, topics, and sub–topics 45

3.2 Literature Review and Research Questions

The scholarship on nationalist parties begins with the concept of party families themselves. While Von Beyme (1985) provided the ﬁrst grouping of political parties into party families, many of the past scholarship focused on social cleavages made similar arguments, grouping parties into various clusters of shared interests, bases of support, and political goals (Lipset and Rokkan 1967; Duverger et al. 1951; Rokkan 1970). In their review of the party family literature, Mair and Mudde (1998b) distinguish between three strategies for dividing parties into families: their historical origin; transnational links; and ideological position. The authors conclude by suggesting that scholars focus on genetic origin and ideological proﬁle, or what parties are based on.

This study describes the ideological proﬁle and rhetorical strategy for nationalist, or radical right, parties. While the very deﬁnition of the party family is hotly contested (see Mudde (1996)), many scholars have focused on the origins and ideological dimension of these parties (Betz 1994; Hainsworth 1982; Minkenberg 2002; McGann and Kitschelt 2005). Irrespective of individual authors’ differences, most agree that the core issue of nationalist parties is immigration, and that they fall somewhere on the right–wing of the political spectrum (Golder 2016).

Much of the previous scholarship on nationalist parties focuses on their electoral rise. These are often differentiated into demand side and supply side explanations. The former describes what drives individuals to vote for these parties (Van der Brug, Fennema and Tillie 2000, 2005; Ryd- gren 2008; Lucassen and Lubbers 2012). This chapter follows scholars such as Fennema (1997) in investigating the supply side factors, or the particular attributes of the parties themselves in order to gain a better understanding of nationalist parties political communication and ideological positioning within parliament. In a pathbreaking work, Kitschelt and McGann (1997) describes the nationalist parties’ ideology as “a new winning formula” that combined free market economics with populist appeals to national purity. 46

Mudde (2010) goes further in explicating the ideological foundations of nationalist parties, defining three ideological tenets upon which nationalist parties rest: immigration; authoritarianism; and populism. Of those three, immigration is probably the most cited and studied feature of the nationalist political imagination (Mudde 1999). Throughout different political systems and historical context, nationalist parties have been able to set the agenda and define the terms of debate over immigration related issues (Williams 2006). In response to their strength on this issue, Van Spanje (2010) finds that nationalist parties force entire party systems to shift their discussion to pay more attention to immigration related topics. In a study of Swiss parties, Skenderovic (2007) shows that nationalist parties in Switzerland are able to relentlessly emphasize the concept of “over–foreignization” to instill fear and exclusionary sentiment within the population and bolster their electoral results. The connection between nationalist parties and immigration is so strong that many scholars have linked greater rates of immigration with greater support for nationalist parties, particularly when unemployment is high (Arzheimer 2009; Knigge 1998; Golder 2003).

The second tenet of nationalist parties identiﬁed by Mudde (2010) is authoritarianism. One of the central theses of Kitschelt and McGann (1997) work is that there is a reorienting of political polarization that now ﬁnds left–libertarian parties competing against right–authoritarians. An authoritarian disposition to prioritize law and order is a bedrock conservative value, pervasive across the entire right–wing of the political spectrum (Layton-Henry 1980). Altemeyer (1988) shows how these authoritarian impulses are grounded in a psychological sense of fear and ag- gression, two qualities often associated with contemporary nationalist parties (De Vreese and Boomgaarden 2005). In addition to the inherently reactionary politics of anti–immigration, nationalist parties often emphasize crime and a perceived sense of lawlessness, with little concern for civil liberties or vulnerable groups that might be caught up in these activities (Smith 2010). Indeed, to nationalist parties, authoritarian appeals to law and order are synonymous with anti– immigrant messaging, as immigrants are framed as outsiders and harbingers of crime (Rydgren 47

2008).

The ﬁnal component of nationalist parties’ political appeals is populism. Perhaps even greater than the deluge of scholarship on the radical right is the recent explosion of interest in populism (Mudde 2004; Mudde and Kaltwasser 2012; Wodak, Khosravinik and Mral 2013; Betz and Immerfall 1998). Unlike nationalism or anti–immigration sentiment, populism is not associated with either a left or right wing dimension, but cuts across traditional political cleavages (Stavrakakis and Katsambekis 2014). Populist political communication posits a homogeneous and pure people, underrepresented and disrespected, in inextricable conﬂict with a corrupt and malevolent elite (Taggart and Szczerbiak 2001). As Goodwyn (1976) argues, populists believe that there is a pure will of the people, which establishment politicians seek to circumvent through opaque legislative institutions. Therefore, a key feature of populist discourse is representation and the need for representatives to let the will of the people be heard.

The literature on supply–side factors and the ideology of nationalist parties is very thorough. The goal of this paper is not to correct previous theoretical conceptions, but rather to show how these ideological tenets are refracted through the strategic prism of parliamentary discourse, using a new dataset and new methods that allow cross–national comparisons. Ideology is not synonymous with rhetorical strategies within parliament; however, the relative emphasis or de– emphasis placed on a variety of issue areas allows a spatial representation of political competition that is often interpreted with an ideological lens (Lowe et al. 2011b). While there is an almost universal understanding of a left–right, capitol–labor dimension, scholars have argued about which other dimensions deﬁne this policy space, or provide grounds for ideological confrontation (Warwick 2002; Van der Brug and Van Spanje 2009; Hix, Noury and Roland 2006; Bakker, Jolly and Polk 2012). The most common inputs for the ideological scaling of parties are manifestos, roll call votes, and expert surveys (Shor and McCarty 2011; Budge 2000; Jahn 2011).

This study departs from these previous attempts at ideological scaling by using parliamentary 48 speeches, without any accompanying voting information. Like all internally generated sources of data on parties’ policy positions, legislative speech is shaped by both ideological elements and strategic considerations. In her oft–cited work on strategic positioning within parliament, Meguid (2008) provides a three-part model of how parties navigate the issue space: position; salience; and ownership. This model provides considerable analytical leverage in the study of niche parties, such as the nationalist parties under consideration here. It requires that these parties demonstrate their issue ownership in order to distinguish themselves from mainstream parties. Mainstream parties can choose dismissive, accommodative, or adversarial response. In some instances it is useful for mainstream parties to boost niche party messages if that niche party will impinge on their main competitor. In this conception, nationalist parties can be seen as “issue entrepreneurs”, who attempt to lay claim to issues such as immigration as a lever to distinguish themselves for other right–wing parties (De Vries and Hobolt 2012a).

3.2.1 Contributions and Research Design

This study does not seek to redefine the ideological tenets of nationalist parties. Its contributions are threefold. First, I seek to confirm existing theoretical interpretations using a novel data set and novel methodology. This will show that, from a spatial perspective, nationalist parties take a right–wing position on issues of immigration, authoritarianism, and populism. This right– wing position is slightly more extreme than mainstream center–right parties on the issue of immigration, and in line with center–right parties on authoritarianism. Additionally, it will show that nationalist parties adopt a uniquely powerful focus on issues that evoke fear of personal safety and personal disgust at outsiders. While past studies have used expert surveys and the analysis of manifestos, this is the first study to illustrate how these ideological concepts play out in a parliamentary setting.

The main goal of this study, building off the common semantic space describe in the previous chapter, is to provide a spatial perspective on inter–party competition. That is, much attention is 49

Table 3.2: A spatial theory of nationalist issues

Anti–immigration Authoritarianism Populism Center–right aligned equivalent — Right–wing competitors oppositional oppositional — Leftists — — divergent paid in this chapter to the spatial relationship of competing parties, and how they align on different substantive axes. I suggest that nationalist parties emphasize immigration as a way to gain leverage on their right–wing rivals. This implies that the issues at the center of nationalist parties’ appeals are clearly contested by both nationalists and the mainstream right–wing. However, due to their governing duties, mainstream right–wing parties simply cannot afford to spend as much time on a single issue dimension, such as immigration, and so will always be outﬂanked in that direction. Christian parties, at least those that do not comprise the main center–right party in their country, have a different structural relationship with nationalist parties. They will instead present an adversarial response in an attempt to attract right–wing voters alienated by the nationalist parties’ issues.

The previous postulates apply to core right–wing issues, such as an authoritarian approach to law and order or anti–immigration positions. These are traditionally right–wing issues, and, as such, the ideological competition over them will be deﬁned by strategic posturing primarily between parties appealing to right–wing voters. This is not necessarily true of populism, which does not pit the left against the right, but rather pro–establishment against anti–establishment. The paper will show that indeed such an empirical pattern is revealed within parliamentary speeches, with leftist and nationalist parties in an oppositional relationship to conservative and social democratic parties. However, it will also show that left and right wing parties differ in how they mobilize populist appeals, with the left–populists focused on a message of democracy and material deprivation, and the right populists employing message that focuses on crime and unaccountable, unrepresentative institutions.

Table 3.2 lays out the theoretical expectations of the paper. I suggest that mainstream right–wing 50 parties take up a similar spatial position as nationalist parties with respect to anti–immigration, but less extreme. On issues of authoritarianism, there is very little distinction between mainstream right–wing and nationalist parties, although nationalist parties focus more on individual crimes and mainstream parties focus more on the carceral policies. Right–wing competitors, primarily those in Christian–Democratic parties take an explicitly adversarial spatial position. On matters of immigration, they emphasize the human impact rather than demographic threats, and on issues of authoritarianism, they focus on the victims rather than the perpetrators. Finally, I expect that, spatially, the populist dimension is equally divided between leftist and nationalist parties, with leftists focusing much more on material deprivation and nationalist focusing on representation and political institutions.

The final contribution of the paper is methodological. While topic analysis is very popular in studies of legislative speech (Grimmer 2010b; Quinn et al. 2010; Roberts et al. 2013; Lucas et al. 2015), it is often theoretically fruitful for scholars to explore the internal dimensions of particular topic spaces, rather than the entire legislative corpus. In keeping with the unsupervised, decomposition based model employed in the first chapter, this paper deepens the model by extending it to particular issue spaces. The model developed here is flexible enough to not only compare particular topic areas, as this paper does, but also compare rhetoric from different corpora or temporal divisions.

3.3 Methodological Approach

This chapter follows the methodological framework laid out in chapter one. The difference lies in the scope of the two research questions. The ﬁrst chapter sought to create a lower-dimensional representation of parties’ political speech, encompassing rhetorical and substantive domains. Instead, this chapter seeks to explore speciﬁc issue areas and strategies employed by far-right parties. To accomplish this, I turn to subtopic analysis. 51

As discussed in the previous chapter, the scope of topics is relatively broad. Take, for example, immigration. This topic is most frequently associated with radical right parties. However, not all discussions of immigration bear the demagogic dimensions associated with the radical right. In other words, not all immigration speech is inherently anti-immigration speech. Similarly, the character of anti-immigration speech can be further divided into chauvinistic cultural appeals, economic scarcity arguments, or the language of fear and threats to physical safety. The goal of subtopic analysis is to elucidate these differences in framing. While merely the amount of attention devoted to a certain topic can tell us a lot about a party’s ideological position, subtopic analysis provides deeper insights into how these topics are presented in parliament.

There are three inputs for this process: the full text corpora described in chapter one, along with associated metadata; the document scores derived from ICA; and the document–term matrices for the corpora. Using the PPMI–based document scores described in the previous chapter, the ﬁrst step is to select a group of documents representative of a topic. The goal in this step is not to get every document that is related to a certain topic; such a task would be futile due to essentially arbitrary standards of how high the document score must be to qualify as belonging to a topic. Instead, the goal is to ﬁnd a set of documents that are representative or otherwise emblematic of a topic. To accomplish this, I take all documents within the 97.5 percentile of a given topic. These documents represent a gold standard of documents pertaining to a certain topic. This represents the new training data in the pipeline.

From there, the subtopic analysis pipeline follows the initial methodological strategy laid out in chapter one. First, embeddings are trained separately on each language. There is one key difference in hyperparameter selection for subtopic analysis versus analyzing the entire corpora. For the entire corpora, we used a minimum word count threshold of 100 in order to reduce the inﬂu- ence of rare tokens. Such a restrictive hyperparameter is no longer viable for subtopic analysis, because we are now working with a reduced corpus of representative documents. Therefore, I reduced the minimum required terms from 100 to 2. Additionally, the size of the embedding 52 dimensions was reduced from 300 to 50, to provide more regularization and reduce the potential of overﬁtting a smaller corpus.

Second, the embeddings trained on each constituent language for each separate set of representative documents are then placed within a common geometric space using the Procrustes technique outlined in chapter one. This provides a common embedding space across all languages for each substantive and rhetorical topic.

3.3.1 ICA vs. SVD

There are some necessary tradeoffs inherent in working with a smaller set of representative topics in order to drill down into competing frames within a topic. One of these tradeoffs, as mentioned before, is the need to use a minimum term count of 2 instead of 100. This poses problems for the decomposition strategy employed when analyzing the entire corpora. For that larger set of documents, we used Independent Component Analysis (ICA). One of the main ad- vantages of ICA is that it selects topical dimensions that are roughly of equal importance. When thinking of a non–hierarchical categorization speech into topics, this makes sense. However, such an approach is no longer feasible when the minimum word count is reduced.

To understand why, think of increasing the minimum term count as a regularization strategy. High precision keywords are, almost by deﬁnition, used less frequently. This decrease in use is why they are high precision: the presence of a high precision word greatly increases the likelihood that a given document belongs to a certain topic. By raising the word count, we reduce these high precision words. If we think of text data as a vast clump of heavily used words, surrounding by topically speciﬁc spikes, raising the word count broadens and reduces these spikes. In other words, we can say that a lower minimum word threshold will leave us with noisier, or spikier, data.

It is not an option to use a higher word count: there simply is not enough variation in the doc- 53 uments for words that reach that threshold. To deal with the additional noise introduced by including rare and highly speciﬁc tokens, we turn to Singular Value Decomposition (SVD). Un- like ICA, the components derived from SVD are in descending order of importance (or amount of variation explained in the original data).

Parliamentary speech is inherently noisy data. Party names, official roles, parliamentary proto- col, and arcane legislative language is frequently used, and often in a highly specific manner. Unless rare terms are excluded, this highly specific language will present itself as sharp spikes in the distribution. Under ICA, the algorithm will attempt to force all topics to mimic this highly specific behavior. This results in topics that capture low–level syntactic patterns and formatting quirks, rather than substantive or rhetorical depth.

Table 3.3 compares the behavior of ICA and SVD with a low minimum word count on embeddings trained on the full corpus. ICA produces what looks like gibberish, but is really just patterns of highly speciﬁc language. Because the English examples are generated by the House of Commons, many topics will soley be devoted to different permutations of “Back Benchers.” On the other hand, SVD, despite the inclusion of unusual and precise terms, produces reasonable topics. This is because the “gibberish” can easily be captured by a few singular values, leav- ing the substantive topics, which have greater variance in word choice, to be uncovered by the algorithm. The necessity for ICA components to have equal importance overrides this behavior.2

Due to the reduction in minimum term count during the embedding step, the pipeline continues

2Interestingly, a similar result to SVD on low minimum term counts can be achieved by reducing the number of components in ICA. By making the number of components small (say 25 components for a 300 dimensional embedding), the issue of ICA identifying only highly speciﬁc, aberrant peaks in the distribution is addressed. Because the components must be of equal importance, they are smoothed out to encompass the less speciﬁc substantive and rhetorically relevant topics. More information on this and an example is included in the appendix. The reason that I do not use this is strategy over SVD relatively trivial: because the number of topics has to be reduced, and we are already working in only a 50 dimensional space, ICA with few topics simply does not allow a variety of topics to be produced. For example, when decomposing the Public Health topic into subtopics, ICA with a small number of components produces a topic devoted to risky behaviors like obesity, drinking, and smoking. SVD, with all 50 possible singular values instead of the restricted components in ICA, produces a separate topic for obesity, a separate topic for drinking, and so on. 54

Table 3.3: SVD vs. ICA on embedding space with a minimum term count of two

English keywords for ICA States Bench Kingdom ) Bench Kingdom Benchers Liberal ” Benches Secretary Warm States 8) Benchers Nations Bencher Nations Ireland remarks Secrataries Majesty ( Kingdom Kingdom

English keywords for SVD offences sea tape costs economy sentences wildlife regulation cost deﬁcit sentence coastal bureaucracy money recession offence heritage limit expensive recovery offenders marine bureaucratic incurred borrowing with each combined embedding space decomposed into a new, subtopical space through SVD. This subtopical space is is deﬁned by terms with corresponding weights to each dimension of the hidden layer, much like the original topical space described in the previous chapter. Similarly to Chapter 2, the topics are then coded for topical or rhetorical relevance and scored at both the document level and party level with the Positive Pointwise Mutual Information (PPMI) approach outlined previously. It is important to note that the word scores, obtained from training data on a small gold standard of topically relevant documents, are used to score the entire corpus, ensuring that there is no selection bias from scoring only a few, highly topical documents.

Throughout the rest of the document parent topics, or those topics discovered in the initial ex- ploration of the corpus and outlined in the previous chapter will be denoted in a different font. Subtopics, also referred to as frames if their content warrants it, will be italicized.

3.4 Immigration

More than any other topic, a focus on immigration is the hallmark of nationalist parties. The recent crises in Syria and elsewhere in the Middle East, and the ﬂow of refugees has brought 55 both the issue of immigration and nationalist parties themselves to the forefront of the political conversation in Europe. However, it is insufﬁcient to just state that nationalist parties are anti– immigrant. This is too vague of a descriptor. Do they emphasize economic scarcity or appeals to cultural homogeneity? How do nationalist party appeals differ from other conservative groups? Is their opposition to Muslim immigration grounded in Christian or secular chauvanism?

This section ﬂeshes out the nature of nationalist parties’ appeals on immigration through subtopic analysis. It does so through drilling deeper into two subtopics: immigration and religion. The former is an obvious topic to investigate further. However, it is also important to describe the discourse around religious pluralism in the countries under study. In Germany, for example, the political response was vastly greater around the large numbers of displaced persons at the fall of the Soviet Union, when compared to the migrant crisis starting in 2014.

The investigation of these topics, typically the domain of the right–wing of the political spectrum, shows a clear pattern. First, and not surprisingly, I find that nationalist parties take the most extreme ideological stance against immigration. Second, I show that their position is not dissimilar to that of mainstream right–wing parties, and their differences are in extent of emphasis rather in the specific frames they emphasize. Finally, I find that Christian parties adopt an oppositional approach, employing different frames that are in tension with nationalist depictions of immigration.

3.4.1 Immigration

Table 3.4 shows a sample of subtopical keywords derived from the 0.975 percentile of document scores on the Immigration topic. The type of subtopics gleaned from this analysis, as the reader will find as this chapter continues, can generally be separated into two types. The first are true subtopics, or highly specific frames or lenses through which one could view the given topic. Applications, Vulnerable Groups, and Displaced Persons, for example, would fit into this 56 category of frames.

Additionally, there is a second category of adjacent or co-occuring topics, which are not true subtopics per se, but rather more substantively broad topics that happen to co-occur with high scoring documents. Terrorism and the EU are examples of this. It is important to note that while the entire corpus also has topics on EU and Terrorism, these differ signiﬁcantly from their subtopical brethren. The subtopics are focused on a particular angle insofar as it coin- cides with the parent topic. For example, the subtopic of Terrorism under the parent topic of Immigration has much more to do with domestic security than the parent topic Terrorism, which covers international events and foreign policy with equal measure. Similarly, the EU subtopic focuses heavily on the institution’s response to asylum seekers and refugees, rather than the broader contours of Europe itself. In other words, parent topics and sub–topics are related, but sub–topics should be viewed through the prism of their parent topic.

The subtopics cover a range of issues pertaining to immigration: its economic impacts, humanitarian appeals, international institutions, and particular aspects of the immigration process itself. One interesting distinction to focus on is the separation of topics between the IDP subtopic (In- ternally Displaced Persons) and the Asylum subtopic. The former focuses speciﬁcally on areas that are generating refugees. The keywords, across all languages, refer to camps, water, food, and the particular countries with crises (Burma3, Syria, etc.). The Asylum subtopic, on the other hand focuses on borders and enforcement, with references to the people affected only insofar as they ﬁt into particular legal categories within the immigration system.

Figure 3.1 shows attention to the Asylum subtopic over time. As mentioned in the caption, these ﬁgures should be interpreted relative to their respective country, not as an absolute score of attention over time.4 A few interesting insights can be gleaned from these ﬁgures. In Germany,

3Many of the MPs still use the colonial name instead of Myanmar, but this keyword is indicative of the Ro- hingya crisis. 4Absolute scores would not only be practically difﬁcult to engineer, but not of much conceptual use. Since institutional constraints greatly impact the duration and extent of discussion, it is not theoretically fruitful to compare attention to particular topics with parties in different legislatures and countries. 57

Applications Terrorism Economic Impact Vulnerable Groups application terrorism migration children applications police immigration child passport security economic trafficking case crime net her cases terrorist population women issued orders public victims letter intelligence impact family person terrorists policy trafficked evidence threat debate parents fi § de Sicherheit de Zuwanderung it minori fi lain de Kriminalitat¨ migration nl kinderen fi Valiokunta es seguridad sv invandringen de Frauen sv uppehallstillst˚ and˚ it sicurezza sv invandring sv barn de Verfahren cs vnitra immigration es menores de § de Polizei sv politik de Kinder fi henkilo¨ nl politie cs rustu˚ nl kind nl documenten it polizia it crisi children de Regelung de Bekampfung¨ economic it minore de Fallen¨ terrorism fi talouden it bambini IDP EU Asylum Workers aid European asylum workers camps Europe detention work humanitarian EU immigration employers refugees treaty Immigration employment UN opt removal labour crisis ” border jobs refugee states Scotland employer Syria countries Scottish pay conflict Union borders working International movement Border job de Fluchtlinge¨ it europea it CIE it lavoratori es refugiados cs Evropske´ it centri it lavoro de Fluchtlingen¨ de Europa nl vreemdelingen cs prace´ sv UNHCR en European es inmigrantes nl arbeid de Hilfe cs unie es inmigracion´ en workers de Kosovo fi Euroopan it immigrati sv arbetsgivare nl vluchtelingen it direttiva en asylum cs pracovn´ı es asilo it europeo it accoglienza es trabajo sv flyktingar es Union´ it dell’immigrazione nl arbeidsmarkt es ayuda de europaischen¨ en detention es empleo

Table 3.4: Immigration keywords 58

Figure 3.1: Scores for Asylum over time for each country

Asylum Scores, United Kingdom Asylum Scores, Sweden 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Asylum Scores, Netherlands Asylum Scores, Italy 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Asylum Scores, Finland Asylum Scores, Spain 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Asylum Scores, Germany Asylum Scores, Czech Republic 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year 59 which does not have a nationalist party represented in the legislature in the time period under study, discussions of asylum peaked in the early ’90s during uniﬁcation, with comparatively more attention paid by the (at the time) left–wing opposition. In the Czech Republic, where nationalist parties were infrequently seated in parliament, there is relatively constant discussion of asylum, with the nationalist parties giving the most attention to the topic in recent years. Perhaps most striking is Italy, which has the largest discrepancy over which parties discuss asylum. This is for a few reasons: a relatively large and electorally successful nationalist party in Lega Norde; a more recently updated corpus that includes the 2015-2016 heights of the migrant crisis; and a geographic position that made the discussion of asylum much more salient in Italy than it was in, say, Sweden.

While not as pronounced as the attention paid to asylum by nationalist parties, conservatives also paid considerable attention to asylum claims. The exception to this is Germany, where discussion of asylum is prioritized by left–wing parties. In countries with sizeable nationalist parties, such as Italy and the Netherlands, conservatives often pay more attention to asylum than average, particularly post-2010. This suggests that this particular issue is not fully dominated by the radical–right.

As discussed, the language used to describe the Asylum subtopic contained little reference to the humanity of migrants, and mostly focused on enforcement strategies and border controls. This is in stark contrast to the subtopic Vulnerable Groups, which focuses on personal references to those who suffer the most in the international migration system. Figure 3.2 focuses on attention to this topic over time, within countries. With the sole exception of Sweden, the general trend within each country is for attention on vulnerable groups to increase over time.

In Germany, where the nominally Christian CDU ﬁlls the traditional center–right role, discussion of vulnerable groups is concentrated amongst the leftist and green parties. In countries that have more paradigmatic Christian parties, these parties pay far more relative attention to vulnerable groups than other party families. This suggests that Christian parties adopt an ad- 60

Figure 3.2: Attention to Vulnerable Groups within the Immigration topic

Vulnerable Groups Scores, United Kingdom Vulnerable Groups Scores, Sweden 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Vulnerable Groups Scores, Netherlands Vulnerable Groups Scores, Italy 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Vulnerable Groups Scores, Finland Vulnerable Groups Scores, Spain 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Vulnerable Groups Scores, Germany Vulnerable Groups Scores, Czech Republic 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year 61 versarial strategy to nationalist parties, who pay the least attention to this subtopic, emphasizing competing narratives when it comes to immigration.

Figure 3.3 shows a SVD decomposition of the substantive immigration subtopics. The length of the arrow and size of the subtopic label indicate the magnitude and direction of a topic’s loading on the dimensions. We can see that the tension between Christian parties and nationalist parties form opposite clusters, one dealing with rates and statistics (speciﬁcally those focusing on demographic change or crime), and the other focused on vulnerable groups such as women, children, or victims of trafﬁcking. There is an orthogonal axis of competition contrasting the Conservatives in the UK with leftist, agricultural, and social democratic parties, the latter of which are focused on the economic impact (largely positive) of immigrants and issues related to workers.

The y-axis represents a focus on impacts, such as economic impacts, immigration related statistics, and terrorism versus a focus on the individuals affected, such as IDP, individual applications, and healthcare. The dimension on the x-axis delineates threats and means of mitigation from beneﬁcial or sympathetic frames of immigration. Notably, this horizontal dimension maps closely to a Left-Right cleavage, with the exception that, due to their focus on vulnerable populations, Christian parties are judged as further left.

At least on the topic of immigration, nationalist parties do not seem to suggest qualitatively different messages than conservative parties; they just devote more attention to these topics. While Lega Norde and the PVV in the Netherlands are perhaps the most “right–wing” according to this scaling, other nationalist parties do not differ greatly from other anti–immigrant parties such as Italy of Values (IdV) or the Five Star Movement (M5S). This lends itself to the position that issue ownership over immigration, and ownership over an anti–immigration stance in particular, is still hotly contested, with nationalist parties having to strongly emphasize anti–immigrant frames to compete with conservatives and other right–wing parties. 62

Weighted SVD of Mean Party Scores

sv_SD 0.3 Statistics uk_UKIP ● Ecological Economic Impact uk_Ind Lab ● Left ● Social Dem. ● nl_PvdD uk_SNP Liberal ● Christian Dem. ● Conservative ● fi_Nuor Nationalist 0.2 Workers ● Agrarian it_PT ● Ethnic/Regional nl_LPF Local es_IU nl_PVV de_CDU/CSU it_LN sv_C uk_Lab/Co−op Identification sv_NyD it_PRC 0.1 de_SPDuk_Greenfi_Kesk it_SC Terrorism cs_VV it_SEL es_CiUit_MDPit_PdCI cs_CMUS cs_SPR−RSCcs_ODA sv_M cs_CSSD it_AN/FdI Asylum EU cs_KSCMcs_Usvites_PSOE sv_SAPnl_GL fi_Kok cs_ANOes_PP it_P−UDEUR fi_RKP it_PD fi_SDP it_DCA−NPSI nl_SP it_IdV it_RnPit_FLI fi_Vas cs_US cs_KDS fi_Vihr 0.0 it_UdCit_PI/DES−CD uk_DUP fi_LKP Elections nl_D66de_FDP es_CDS sv_L cs_ODS

Dimension 4 nl_PvdA uk_Labcs_TOP09 cs_KDU−CSLit_FdV nl_VVD it_AP/NCD fi_PS it_M5S de_Gruene nl_CDA fi_KD uk_Con de_Linke IDP nl_SGP it_FI/PdL

−0.1 uk_Plaid

nl_CU sv_MP uk_SDLP

uk_LibDem Healthcare sv_KD sv_V

−0.2 es_PNV Vulnerable Groups Applications −0.3

−0.3 −0.2 −0.1 0.0 0.1 0.2 0.3

Dimension 3

Figure 3.3: Immigration scaling 63

3.4.2 Religion

While immigration might be the most emphasized plank of nationalist parties’ ideology, their rhetorical positioning in parliament is not deﬁned by a blanket anti–immigration position. As noted in the literature, and alluded to by the name of the party family itself, nationalist parties are particularly attuned to threats of perceived national integrity. This xenophobic strain of rhetoric is not only, of course, anti–immigrant, but rather heightens its opposition according to the cultural distance of would–be migrants. In most European countries this implies a focus on Islam and Muslim migrants in particular.

There are two potential rhetorical strategies that parties could use to appeal to nationalistic sense of Christian identity. One is, as stated, to appeal to anti–Muslim sentiment and frame any increased Muslim presence as a threat. The other would be to emphasize Christian identity through positive nationalistic appeals. While these two strategies are not mutually exclusive, speaking time is a ﬁnite resource. I ﬁnd that nominally nationalist parties place far more emphasis on Muslim immigration, with little attention paid to positive nationalistic appeals. However, as with anti–immigration, there does not seem to be a clear spatial break between nationalist and mainstream right–wing parties, with the former taking only a more extreme, rather than substantively different, stance.

Table 3.5 illustrates a sample of religion subtopics. The topics encompass issues of more explicitly religious issues, such as charity and Christianity, as well as questions where religious concerns play a strong role in public policy, such as gender, families, or education. Topics such as immigration and violence are issues that are co-occuring with religion; in this case it is the discussion of speciﬁc religious groups that causes these documents to score highly along this dimension. Interestingly, appeals to democracy represent their own subset of religious documents due to the rhetorical and ideological overlap of supposed Western and Christian values.

Two of these subtopics warrant further investigation: the immigration subtopic, connoting neg- 64

Charity Families Violence Immigration sector family offence immigration work children behaviour British charities marriage violence country organisations child crime English building parents criminal citizenship development married police Home organisation families offences citizens volunteering father hatred UK role couples victims migration funding forced extremism here fi kulttuurin fi lapsen de Gewalt nl inburgering de Arbeit it famiglia sv brott en immigration sv verksamhet de Ehe sv vald˚ it cittadinanza de Bereich sv barn nl geweld es nacionalidad sv ideella nl kinderen de Polizei nl integratie it attivita` nl huwelijk es penal de Deutschland de Zusammenarbeit nl kind de gegen sv Sverige sv folkbildningen it figli it violenza nl Nederlanderschap sv idrotten sv aktenskap¨ it reati de Staatsangehorigkeit¨ it sviluppo it matrimonio es delitos de Integration Democracy Christianity Schools Gender political churches education women democracy Christian schools gender election Church school equality civil church teachers female party religious students male politics faith university men public Christians pupils discrimination vote Catholic curriculum woman Labour organisations universities representation democratic religion Education pay de Demokratie sv kyrkan nl onderwijs de Frauen it democrazia cs c´ırkve nl scholen fi tasa it politica fi kirkon sv skolan it donne de Burger¨ cs c´ırkev it scuola sv kvinnor de Verfassung es Iglesia es educacion´ fi naisten nl vrijheid cs c´ırkv´ı en education nl vrouwen nl politieke it cristiani sv elever en women sv politiska cs majetku en schools es mujeres sv demokratin en churches nl school it donna sv demokrati sv kristna fi kielen sv jamst¨ alldhet¨

Table 3.5: Religion keywords 65

Figure 3.4: Attention to Immigration within the Religion topic

Immigration Scores, United Kingdom Immigration Scores, Sweden 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Immigration Scores, Netherlands Immigration Scores, Italy 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Immigration Scores, Finland Immigration Scores, Spain 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Immigration Scores, Germany Immigration Scores, Czech Republic 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year 66 ative appeals about Muslim immigration; and the Christianity subtopic, representing a positive, nationalistic appeal. Figure 3.4 illustrates how much attention was devoted to the immigration frame within religious topics. Nationalist parties dominate this subtopic, consistently devoting a disproportionate amount of their time to it in the Netherlands, Sweden and Italy. In almost all cases, the subtopic is almost completely avoided by social democratic or Christian parties. In the UK, it is most frequently invoked by the DUP, a functionally British nationalist party that shares a similar ideology as members of the radical–right.

Compare this with Figure 3.5. This subtopic, positive appeals and references to Christianity or a Christian heritage, is, as could be expected, dominated by Christian parties. This is not true of the larger, center–right Christian parties such as the CDU in Germany or the CDA in the Netherlands. While nationalist parties in Italy pay somewhat more attention to Christianity than other parties, this effect is both very recent and not particularly signiﬁcant. This suggests that nationalist appeals to Christian chauvinism are limited and strongly outweighed by anti–Muslim, anti–Immigrant appeals.

Figure 3.6 displays the SVD scaling results from the substantive religious topics. Similarly to the immigration topic, there seems to be a juxtaposition between Christian parties and nationalist parties that is loosely orthogonal to a more traditional Left–Right split. Not surprisingly, nationalist parties score highly according to the immigration subtopic vector. The dimension along the x-axis seems to divide the space between traditional, or conservative, values and more liberal, social justice oriented, religious values. The y–axis seems to distinguish secular from religious discussions, with gender, discrimination, and violence all loading heavily along this dimension, contrasting with families and Christianity.

Similar to the findings in the Immigration topic, we find that in this instance, nationalist parties are filling a far–right role; operating in a direction similar to the Conservative parties, just more extreme. It is important to note that there are a couple of exceptions to this: the two Czech nationalist parties, Usvit and SPR-RSC. Immigration has not been a major political issue 67

Figure 3.5: Attention to Christianity within the Religion topic

Christianity Scores, United Kingdom Christianity Scores, Sweden 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Christianity Scores, Netherlands Christianity Scores, Italy 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Christianity Scores, Finland Christianity Scores, Spain 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Christianity Scores, Germany Christianity Scores, Czech Republic 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year 68

Figure 3.6: Religion scaling

Weighted SVD of Mean Party Scores 0.3 Families nl_PvdD ● Ecological uk_Ind Lab nl_SGP ● Left ● Social Dem. nl_CU sv_KD ● Liberal it_MDP ● Christian Dem. ● Conservative ● Nationalist 0.2 uk_DUPuk_SDLP ● Agrarian es_CDS cs_VV it_AP/NCD ● Ethnic/Regional

Christianity Medical fi_RKP fi_Nuor fi_LKP it_UdC cs_CMUS fi_KD it_SEL sv_C sv_MP cs_Usvit it_PI/DES−CD uk_UKIP it_PT cs_TOP09 it_M5S

0.1 International

uk_Green it_FI/PdLcs_KSCMde_SPD de_FDP es_CiU it_RnP cs_SPR−RSC cs_ANOit_PD de_CDU/CSUnl_CDAit_FLI sv_V cs_KDU−CSL it_DCA−NPSI Charity uk_LibDem cs_CSSDde_Gruenees_IUnl_D66 0.0 it_FdV fi_PS Military es_PNV fi_Vihr uk_Con it_P−UDEUR fi_Vas it_SC cs_ODA sv_M cs_US uk_Lab/Co−op sv_NyD cs_ODS it_PdCI it_PRC nl_SP es_PP es_PSOEnl_PvdA Secular ~ Religious fi_Kesk uk_Plaidit_IdV nl_GL sv_L Discrimination fi_SDP de_Linke uk_SNP fi_Kok sv_SAP it_AN/FdI uk_Lab −0.1 sv_SD nl_VVD Schools

nl_LPF

nl_PVV Violence −0.2 it_LN cs_KDS Immigration Gender −0.3

−0.3 −0.2 −0.1 0.0 0.1 0.2 0.3

Traditional Values ~ Social Justice 69 in the Czech Republic in the timeframe under consideration. These parties nationalist appeal lies more in their antagonism to the country’s Roma minority, rather than a xenophobic program against Muslim immigrants as pursued by the more typical nationalist parties, such as the Lega Norde and PVV.

A consistent pattern emerged from the spatial analysis of the two issue areas under discussion: religion and immigration. In both cases, nationalist parties formed an extreme end of one spectrum, with mainstream conservative parties in a similar direction at a lower magnitude, and Christian parties on the opposite pole. This suggests that mainstream, right–wing parties are aligned with the nationalist parties on the issue of anti–immigration, and there is no evidence to indicate they take a qualitatively different position; whereas Christian parties take an oppositional approach, emphasizing subtopics that are spatially opposed to those most frequently discussed by nationalist parties..

3.5 Authoritarianism

The second pillar of nationalist parties ideology, as described by Mudde (2010) is authoritarianism. Typically, this is operationalized as a focus on law and order. While nationalist appeals have an intrinsically authoritarian connotation, this section will focus on parties’ rhetorical strategies for topics on crime and public health. The former is a clear vector for authoritarian rhetoric, inculcating a sense of fear in voters and position the speaker’s party as a bulwark against chaos and disorder. While seemingly innocuous, Public Health, which deals with unsavory, petty criminal behavior such as drug use and prostitution, can also lend itself to authoritarian frames, particularly those that appeal to voters sense of disgust or experience with nuisances or what they consider to be immoral behavior.

Contrary to expectation, I ﬁnd that within the Crime issue area, nationalist parties take a po- 70 sition in line with their mainstream right–wing opponents. Where they distinguish themselves is with a relentless focus on the crimes themselves, particularly within the framing of crime as a disease or other social ill. This suggests that they are doing so to emphasize fear and disgust, rather than a positive appeal to law and order.

3.5.1 Crime

We start our discussion on competiion along authoritarian political dimensions with the Crime parent topic. Table 3.6 presents the keywords of selected subtopics within this issue area. The subtopics Prison, Police, and Courts all describe different components of the criminal justice system. Note that the keyword “court” is found in both the Courts and Rights subtopics. This hints at an opposition between these two topics, with the former much more focused on criminal proceedings (and therefore more authoritarian in context), and the latter focusing almost exclu- sively on civil liberties and the rights of defendants. Similarly, the keywords of the superﬁcially neutral Prison subtopic betray its ideological leanings; it is as much about the offenders, length of their sentence, and possibility of reoffending than the neutral administration of prisons.

They keywords for the subtopic Local also illustrate the dynamic of co–occurring topics discussed previously. Local, as a subtopic of Crime, is different from the parent topic Local. Instead, it can be seen as local issues refracted through the prism of Crime, or, to put it differ- ently, using locality as a framing to discuss criminal issues. In this case, those issues that have a a more local impact are gangs, antisocial behavior, and wayward youths.5

One of the hallmarks of an authoritarian approach to law and order is an overemphasis on punishment and punitive sanctions. The frame within the Crime parent topic that most closely approaches this punitive dimension is the Prison frame.6. Attention over time to this punitive

5For the curious reader, an ASBO, one of the top english keywords in the “Local” subtopic, stands for Anti- Social Behavioral Order. It’s a non–criminal court order designed to address socially deleterious behaviors without punitive criminal intervention. 6If the emphasis on reoffending, sentencing, and sanctions is not enough to convince the reader that this 71

Courts Prison Victims Police court prison victims officers trial prisoners victim police justice offenders trafficking officer judge prisons slavery forces Court sentences women Police jury sentence domestic force courts probation rape constable trials sentencing forced armed system reoffending trafficked London witnesses custody family policing cs rˇ´ızen´ı sv kriminalvarden˚ de Opfer fi poliisin de Verfahren it detenuti it vittime fi poliisi cs soud es penas es v´ıctimas cs policie it processo es prision´ nl slachtoffers sv polisen de Justiz en prison de Frauen de Polizei de Richter cs trestu nl slachtoffer sv poliser cs soudu sv intagna sv brottsoffer nl politie it giudice it carcere en victims it polizia es juez nl tbs it donne en officers en court sv fangelse¨ es mujeres en police Rights Offenses Local Drugs rights offences local drug public serious council alcohol safety crime antisocial cannabis state crimes ASBOs test liberties Crime gang trafficking freedom organised constituency prostitution Court offence areas driving civil penalty youth substances Rights criminals CCTV slavery law CCTV terrorism drugs it Costituzione de Kriminalitat¨ sv ungdomar en drugs cs prav´ it reati sv unga nl drugs es derechos de Straftaten en local es droga de Freiheit sv brottslighet it locali it sostanze de Staat nl misdrijven nl gemeenten sv narkotika nl overheid es penal nl ouders en drug sv grundlaggande¨ de Verbrechen en council es narcotrafico´ es Constitucion´ sv straff nl gemeente it stupefacenti nl rechtsstaat nl criminaliteit nl overlast es drogas de Bundesverfassungsgericht es terrorismo it enti it droghe

Table 3.6: Crime keywords 72

Figure 3.7: Attention to Prison within the Crime topic

Prison Scores, United Kingdom Prison Scores, Sweden 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Prison Scores, Netherlands Prison Scores, Italy 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Prison Scores, Finland Prison Scores, Spain 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Prison Scores, Germany Prison Scores, Czech Republic 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year 73 frame is shown in Figure 3.7 What is immediately striking about this ﬁgure is that the party most emphasizing this punitive frame is almost always the main center–right party. The nominally Liberal party scoring highly on this subtopic in the Netherlands is the VVD, a party that led a relative straightforward center–right coalition from 2012 until the end of the data collection period.

Interestingly, very few nationalist parties seem to emphasize this frame more than other conservative parties.7 In the Netherlands and Italy, both with a signiﬁcant number of nationalist MPs, other right–wing parties routinely emphasized prisons and incarceration at a higher rate than nationalist parties. In Spain, where there are no nationalist parties represented over the time period, the main divide in emphasis on punitive measures seems to be between centralizing and autonomist factions, with the PSOE and PP routinely emphasizing prison more than the regional parties.

The other side of a focus on punishment, as captured through the Prisons frame, is a focus on the crimes themselves. This is captured with the offenses subtopic, shown in Figure 3.8. While appeals to punishment might adhere more closely to the appeal to order at the core of authoritarian ideologies, an emphasis on the crimes themselves might lend itself to inculcating fear, a key strategy of authoritarian parties Smith (2010).

Whereas nationalist parties did not seem to emphasize prisons or punishment at a greater level than other right–wing parties, they clearly devote more speaking time to offenses than other party families. This is true most drastically in Italy, but also Sweden, the Netherlands, and the DUP in the UK devote more time to discussing offenses than other parties. Unlike the prison subtopic, Christian parties do not emphasize crimes. And while there are some instances of mainstream conservatives emphasizing crime at a disproportionate rate, particularly in Sweden, they are not nearly as vocal as they are about punishment or other punitive measures. subtopic is about punitive measures, one of the top English keywords that just missed the top ten cutoff for the keyword table is “tough”. 7With the exception of the DUP during the early 2010 period. 74

Figure 3.8: Attention to Offences within the Crime topic

Offences Scores, United Kingdom Offences Scores, Sweden 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Offences Scores, Netherlands Offences Scores, Italy 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Offences Scores, Finland Offences Scores, Spain 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Offences Scores, Germany Offences Scores, Czech Republic 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year 75

Weighted SVD of Mean Party Scores 0.3

Victims ● Ecological ● Left uk_Lab ● Social Dem. uk_Lab/Co−op ● Liberal ● Christian Dem. ● Conservative ● Nationalist 0.2 ● Agrarian ● Ethnic/Regional it_P−UDEUR it_AP/NCD

it_PI/DES−CD it_PRC it_DCA−NPSI

sv_KD fi_KD nl_PvdD it_FdV 0.1 Police Information es_PSOEes_PP Prison es_CiU Drugsfi_Kok nl_PVV it_SEL cs_Usvit cs_CMUS de_Linke uk_LibDem fi_RKP es_IU de_SPDsv_C Immigrationcs_SPR−RSC sv_SAPnl_SP it_MDP cs_KDU−CSLfi_SDPcs_ODSfi_Kesk uk_Green it_SC cs_TOP09nl_CU de_CDU/CSU sv_V uk_SNP 0.0 sv_Lcs_CSSDcs_KDSit_LN sv_SDit_PdCIfi_PS es_CDS it_RnP nl_PvdA cs_KSCMfi_Vihrfi_Vas nl_GLnl_CDAnl_LPF EU Local de_Gruenenl_D66 sv_M uk_Con uk_DUP sv_MP it_UdC cs_US it_AN/FdI

Courts ~ Victims nl_SGPcs_ANO Rightsde_FDP

cs_ODA nl_VVD it_PD sv_NyD cs_VV fi_Nuor it_PT uk_Plaid Offences −0.1

it_FI/PdL uk_UKIP Political Violence it_IdV fi_LKP

it_M5S−0.2

uk_SDLPit_FLI Courts uk_Ind Lab

es_PNV −0.3

−0.3 −0.2 −0.1 0.0 0.1 0.2 0.3

Effects of crime ~ Offenders

Figure 3.9: Crime scaling 76

Figure 3.9 provides the SVD decomposition of the Crime parent topic. The x–axis distinguishes between the broader social context of crime, on the negative side, and particular topics dealing with offenders, such as police, courts, and offenses, on the positive side. The y–axis is oriented mainly along the tension between courts and victims, with subtopics without a particular ideological valence, such as local running orthoganol to this axis. The latent dimensions of party competition within the criminal space show an authoritarian divide, with conservative parties anchoring one end, and leftist parties, such as Linke, and Christian parties, which tend to emphasize victims and vulnerable groups, on the other end. Regional parties, which often emphasize their historical separatist struggles are unsurprisingly oriented in the same direction as the Politcial Violence frame, with Liberal parties loading strongly towards Courts, and largely less authoritarian than conservative parties.

In this lower dimensional representation of discussions around crime, it does not seem that nationalist parties exhibit any greater authoritarian tendencies than other right–wing parties. Archetypal nationalist parties, such as the True Finns in Finland in PVV in the Netherlands are not rated as more authoritarian than mainstream conservative parties such as the Tories, the Moderate Party in Sweden, and the CDU in Germany.

3.5.2 Public Health

Authoritarian appeals go beyond a stringent enforcement of law and order, it also entails a removal of undesirables and an idea that the nation can be puriﬁed of other nuisances (Skenderovic 2007). While Public Health might not immediately come to mind as a vector for authoritarian rhetoric, it covers many frames that invoke a lack of safety, uncleanliness, and a disease–like interpretation of outsiders contaminating otherwise safe and peaceful areas. Nationalist parties, and others with authoritarian leanings are likely to employ such rhetorical frames.

Keywords for subtopics from the Public Health parent topic are show in Table 3.7. Many 77

Weapons/Disarmament Crime Consumers Animals weapons police consumers animals knife criminal consumer animal guns law market dog gun officers information dogs firearms crime prices farmers knives tickets price welfare arms prison retailers pubs nuclear Act buy meat international offence supermarkets disease security court labelling Act de Waffen sv brott de Verbraucher nl dieren sv vapen nl rechter sv konsumenter nl dierenwelzijn es armas sv polisen sv konsumenterna it animali de Staaten cs policie es consumidores nl dier cs zbran´ı it reato nl consument de Tiere de Abrustung¨ sv narkotika it consumatore en animals it armi it reati en consumers en animal en weapons en police it consumatori es animales sv krigsmateriel nl politie sv konsumenten sv djur nl wapens nl boete es precios en dog Smoking Gambling Alcohol Children tobacco gambling alcohol children packaging betting drinking school cigarettes online drinks child smoking advertising drink parents Bill Gambling night schools legislation internet minimum families measures companies licensing Children advertising industry pricing education cigarette operators disorder home introduced pub binge obesity nl maatregelen sv spel fi alkoholin de Kinder sv forbud¨ it gioco en alcohol sv barn en tobacco en gambling fi alkoholia fi lasten cs vyrobk´ u˚ it giochi nl alcohol nl kinderen sv amnen¨ sv spelberoende sv alkohol en children sv kemikalier en betting en drinking fi nuorten en packaging it societa` es tabaco de Kindern sv mot sv Svenska fi alkoholijuomien sv barnen en cigarettes en online fi alkoholi nl ouders en smoking sv reklam cs alkoholu sv for¨ aldrar¨

Table 3.7: Public Health keywords 78 cover typical vices that pose public health issues: Smoking, Gambling, and Alcohol among them. Others, such as Children or Consumers focus on potential victims or those who might be threatened. Other topics, such as Animals or Weapons deal with particular industries which often have public health implications. Note that the keyword “Act” appears for both the Animals and Weapons subtopic, implying that these topics are often discussed with reference to particular legislation.

Many of these topics, while they demonstrate the effectiveness of the subtopic analysis approach employed throughout the paper, do not speciﬁcally emphasize authoritarian tendencies or other topics of interest to the study of nationalist parties. The exception to this is the Crime subtopic. Note that this differs from the Crime parent topic, discussed in the previous section. Rather, it is helpful to think of this as Crime expressed through the prism of public health, entailing on emphasis on disease–like language, emphasizing a more immediate or personal impact, and a particular moral valence on crime that places them it in a similar rhetorical space as vices such as smoking, gambling, or excessive drinking.

Figure 3.10 shows attention to this topic over time. Across all countries with significant nationalist parties, these parties devote far more attention to this topic than other party families. Italy has perhaps the largest discrepancy, with Lega Nord joined by their populist bretheren M5S. The True Finns in Finland and Usvit in the Czech Republic also devote a disproportionate amount of attention to this frame. Mainstream, center–right parties also devote more attention to this topic than average, but the effect is much more muddled and less extreme than that with nationalist parties. While the Tories dominated discussion of Crime, along with the nationalist–sympathetic DUP, there relative attention paid declined significantly during the Cameron government. A similar pattern can be seen in Sweden, where after taking power in 2006, the Moderate led government was outflanked on this issue by social democratic and left–wing parties.

In stark contrast to the nationalist parties, Green and Agrarian parties are among those that pay the least attention to a criminal framing of public health. A signiﬁcant exception to the 79

Figure 3.10: Attention to Crime within the Public Health topic

Crime Scores, United Kingdom Crime Scores, Sweden 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Crime Scores, Netherlands Crime Scores, Italy 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Crime Scores, Finland Crime Scores, Spain 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Crime Scores, Germany Crime Scores, Czech Republic 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year 80 general right–ward shift in crime rhetoric occurs in Germany where Die Linke is consistently one of the parties most likely to emphasize this framing. Die Linke does not seem to be alone in this among leftist parties. While they do not emphasize the subtopic quite as much as the nationalist True Finns party, Finland’s Left Alliance (Vas) also pays more attention to this topic than others. A possible explanation is that, while far–right parties use a public health framing to describe crime as a contagious disease spread by outside malefactors, left–wing parties might use a health framing to de–emphasize carceral and other coercive solutions to crime. This could also reﬂect a limitation of the sub–topic model, where different viewpoints are condensed along a single topic, when this ought to have been split into multiple dimensions.

Figure 3.11 shows the SVD of all substantive public health topics. The x–axis seems to distinguish between potentially vulnerable groups, such as consumers or children, and nuisances, such as crimes, drugs, gambling, and alcohol. The y–axis seems to divide based on the geographic scope of the issue, with global issues such as the EU, weapons, and consumers on the positive side, and local issues like children or gambling on the negative side. There is not a clear Left–Right dimension to this latent representation. Rather there seem to be three distinct poles. One, led by the nationalist parties, which focuses heavily on crime, with other nuisance topics loading in a similar direction. Another, led by Christian parties and some of the social democratic ones, emphasizes children and their protection. A ﬁnal pole represents Green and agricultural parties. While the Animals subtopic points weakly in this direction, its main feature seems to be a lack of a particular topic, likely owing to the lower level of attention that these party families pay to public health topics.

With respect to the larger issue of law and order or authoritarian politics, this analysis has found that nationalist parties do not diverge dramatically from other parties on the political right. Within the Crime parent topic, they were unique in their focus on offenses, rather other aspects like incarceration. Additionally, they took a spatially extreme position on the parent topic of Public Health, likely due to their unique emphasis on crime as a disease or plague. 81

Weighted SVD of Mean Party Scores 0.3

● Ecological ● Left ● Social Dem. ● Liberal ● Christian Dem. ● Conservative cs_ANO ● Nationalist 0.2 EU ● Agrarian Consumers ● Ethnic/Regional

it_FI/PdL it_PD uk_SNP es_CDSnl_VVD sv_SD it_FLI Weapons uk_SDLP fi_LKP sv_MP cs_TOP09

0.1 sv_M nl_PvdD it_FdV es_PPcs_CSSD it_RnP it_IdV fi_Kesk nl_CDA uk_Plaid fi_Nuor sv_C it_PT it_PRC de_FDP

fi_RKP cs_Usvit nl_GLit_P−UDEURde_Gruene sv_Vfi_Vihr it_PdCIit_LN de_CDU/CSURoads uk_UKIP uk_Green it_UdCcs_US uk_LibDem Housing fi_PS nl_LPF nl_PvdAcs_KDU−CSLit_SEL nl_D66 cs_VV sv_SAP nl_PVV 0.0 cs_ODS uk_ConBusiness es_CiU es_PSOEit_SCde_SPD Smoking es_PNV fi_Kok fi_Vas Crime cs_KDSAnimals cs_CMUS cs_KSCM it_AN/FdI Workplacenl_SP sv_NyD Towns Small Business Local ~ International fi_SDP sv_L Alcoholuk_DUP it_DCA−NPSI cs_ODA it_M5S de_Linke

−0.1 Drugs nl_SGP es_IU it_PI/DES−CD cs_SPR−RSC uk_Lab/Co−op fi_KD nl_CU uk_Lab Gambling

it_MDP it_AP/NCD −0.2

Children sv_KD −0.3

−0.3 −0.2 −0.1 0.0 0.1 0.2 0.3

Health ~ Nuisances

Figure 3.11: Public Health scaling 82

3.6 Populism

The ﬁnal pillar of nationalist ideology, populism, is the most substantively amorphous. Unlike anti–immigrant sentiment or authoritarian preferences for law and order, populism is about a political orientation that positions a pure people against a corrupt elite (Taggart and Szczerbiak 2001). Detecting this dimension of nationalist parties is difﬁcult, as parliamentary speech is more concrete and grounded in particular policies or issue areas.

We will pursue the mapping of a populist dimension in the following way. First, there is one issue area that does concentrate anti–elite sentiment, particularly for nationalist or right–wing parties: European integration. Much of the anti–Europe discourse, especially in recent years, approximates the populist dimension by pitting the downtrodden, “ordinary people” against faceless Brussels bureaucrats (Aalberg et al. 2016). Second, we will explore various rhetorical strategies employed by populist parties. These focus around negative rhetorical strategies Failure and Nonsense, and positive appeals to Representation. All of these can be considered anti–establishment strategies, often castigating the governing parties for being out of touch or failing to allow necessary input from ordinary people.

Despite the inherent difficulty in isolating a populist dimension, the above approach finds considerable analytical leverage. The previously mentioned substantive and rhetorical topics provide a consistent picture of a populist space in parliamentary discourse. Mainstream parties are pitted against parties that almost always find themselves in opposition. However, there is a clear difference between left–wing and right–wing populism. The former places a greater emphasis on material deprivation and the realization of democratic values, while the latter focuses on crime and unaccountability in larger institutions. 83

3.6.1 Europe

The ﬁrst aspect of populist discourse investigated here focuses on parties’ attitudes to European discourse. While there is leftist opposition to the EU on populist lines,8 in the past few decades, such opposition has been a hallmark of right–wing populists (Wodak, Khosravinik and Mral 2013). Opposition to the EU takes a variety of policy angles, but is often focused on immigration, regulations, and, in surplus areas, bailouts and ﬁscal policy.

Table 3.8 displays the keywords from a sample of subtopics drawn from the EU parent topic. Subtopics such as Markets or NATO focus on the regular administration of the EU and recur- ring coordination issues affecting its member states. Some subtopics, like Threats or Eurozone focus on a collection of temporally limited crises, such as the sovereign debt crisis or Russian encroachment. Two subtopics, EU Members and New Members focus on member states, the former dealing with core members and issues facing them, while the latter deals with states on the European periphery and accession debates.

Of particular interest for the scope of this chapter are two almost rhetorical frames: Sovereignty and Others. The former encompasses ﬁrst person plural pronouns, such as “ours”, “us”, and “we.” In contrast, Others scores highly references to third person plural pronouns such as “they” and “their.” The Sovereignty also includes explicit national references, with multiple references to the “home country.” The Others topic, on the other hand, includes references to the people threatened by others, namely “workers” and “citizens”. However, the topic is not entirely xenophobic and anti–immigrant in nature; mentions of “women” and “children” imply references to vulnerable groups, meaning that there can be references to others with both a positive and negative valence. However, terms such as “children” can also signal negative frames, invoking replacement theory or a dimunition of national purity.

Figure 3.13 shows attention to the Others topic over time. Unlike other some of the other

8It could be argued that left–wing opposition to European integration was more substantial than right–wing opposition during the 1970s, when the primary cleavage was market liberalization rather than immigration. 84

Markets Sovereignty Eurozone NATO market our banks defence directive sovereignty financial NATO energy national eurozone forces services us euro security regulation we Bank military competition this fiscal co single UK banking foreign trade House currency war co We IMF Afghanistan companies opt bank treaty it mercato fi me fi euroalueen de NATO nl richtlijn fi olemme fi pankkien cs NATO es mercado cs jsme fi Kreikan nl NAVO nl markt es nuestra nl banken en defence it settore fi Me it banche sv Nato en market fi emme fi EVM fi ulko- cs smerniceˇ fi meidan¨ sv krisen it difesa nl bedrijven sv var˚ fi talouden fi Nato es energ´ıa de unsere es Banco cs obrany it prodotti de uns fi Kreikka en NATO Threats EU Members Others New Members Russia countries people Turkey energy France workers accession referendum Germany Home Croatia Iran member care EU Foreign members they enlargement China Poland their Kosovo Russian French children Ukraine United other citizens membership President constitution opt Turkish business Spain women Member it Russia it Paesi sv manniskor¨ de Turkei¨ fi Venaj¨ an¨ de Lander¨ de Menschen cs republika it sanzioni fi maat nl mensen it europea cs by nl landen it lavoratori fi neuvoston en Russia es pa´ıses sv barn nl Turkije fi Suomen fi maiden cs kterˇ´ı sv Turkiet it russa it Germania it lavoro de Kriterien es econom´ıa es Francia it giovani it Turchia es Unidos sv lander¨ it immigrati sv Ryssland fi Venaj¨ a¨ en countries es personas sv Ukraina

Table 3.8: EU keywords 85

Figure 3.12: Attention to Others within the EU topic

Others Scores, United Kingdom Others Scores, Sweden 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Others Scores, Netherlands Others Scores, Italy 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Others Scores, Finland Others Scores, Spain 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Others Scores, Germany Others Scores, Czech Republic 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year 86 subtopics we have investigated so far, which show the nationalist parties emphasizing the subtopic at a highly above average rate and more mainstream right–wing parties emphasizing the subtopic at an above–average rate, the two primary party families emphasizing references to Others are nationalist and leftist parties. This is true in countries like the Netherlands, with signiﬁcant nationalist party presence, and Germany, without any nationalist parties represented in the legislature. This belies the dual–valence nature of this topic, that it contains both demagogic and sympathetic portrayals of Others. The topic is rarely emphasized by mainstream conservative parties. One notable exception is the PP in Spain, as an opposition party in response to the 2004 Madrid train bombings. It also stands to reason that this sub–topic is more associated with parties in opposition.

Another subtopic that receives disproportionate attention from nationalist parties is New Mem- bers, which focuses on periphery states. In Sweden, the Netherlands, and Italy, there is considerable attention paid to this subtopic, particularly post-2005. This suggests that much of the attention to this topic revolved around Turkey’s accession to the EU. There is a similar spike post-2010, suggestion that concerns of peripheral countries was also a main axis of the debate around the migration crisis. In earlier years, this subtopic seemed to be a more left–wing concern, with Leftist parties in the Czech Republic, Netherlands, and Sweden all mentioning it at a disproportionate rate to other countries, particular during the ongoing crisis involving the former Yugoslavian states.

Figure 3.14 shows the SVD decomposition of the various EU subtopics. In the other substantive dimensions that have been discussed, I have tried to separate the effect of government or opposition status from the parties’ positions on substantive issues. When discussing populist parties, however, this distinction no longer seems relevant. While there are some populist parties that have participated in governing coalitions (and, more recently, some that have reached formed coalitions themselves), there is a reason that the establishment is established: their position in power. 87

Figure 3.13: Attention to New Members within the EU topic

New Members Scores, United Kingdom New Members Scores, Sweden 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

New Members Scores, Netherlands New Members Scores, Italy 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

New Members Scores, Finland New Members Scores, Spain 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

New Members Scores, Germany New Members Scores, Czech Republic 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year 88

Weighted SVD of Mean Party Scores

0.3 es_CDS sv_SD cs_SPR−RSC ● Ecological ● Left ● SovereigntyEU Members it_AN/FdI fi_NuorSocial Dem. ● Liberal fi_LKP cs_Usvit● Christian Dem. ● Conservative ● Nationalist 0.2 sv_NyD ● Agrarian it_M5S ● it_LN New Members Ethnic/Regional nl_PVV

it_IdV it_DCA−NPSI

uk_DUP Eurozone NATO es_PNV de_CDU/CSU sv_M fi_PS it_SC uk_Con 0.1 it_MDP cs_ODA nl_LPF sv_L nl_VVD cs_KSCM Threats it_PI/DES−CD nl_SGP uk_SDLP it_RnP Agreements fi_Vasit_PT nl_CU de_FDP uk_SNPcs_KDS it_SEL cs_ODS it_UdC nl_CDA de_Gruenecs_CMUS nl_SP uk_Lab/Co−op nl_D66 it_FI/PdL it_FLI 0.0 nl_PvdA fi_SDP es_CiU cs_CSSD uk_Green fi_RKP de_SPD fi_Vihr cs_TOP09 fi_Kok cs_KDU−CSL it_AP/NCD uk_Lab sv_Vit_PRC nl_GL it_PdCI cs_US uk_Plaid cs_ANO sv_SAP es_PP cs_VV de_Linke sv_KD Institutions ~ Sovereignty Voting fi_KDuk_LibDem es_IU −0.1 it_PD es_PSOE Intl Crises Fisheries sv_MP Others it_FdV it_P−UDEUR sv_C fi_Kesk nl_PvdD −0.2 Markets

Summits −0.3

−0.3 −0.2 −0.1 0.0 0.1 0.2 0.3

Government ~ Opposition

Figure 3.14: EU scaling 89

In this spatial representation of parties’ rhetorical positioning within the EU topic space, the x–axis divides opposition from governing parties, or anti–establishment from populist parties.9 The y–axis maps somewhat onto a traditional left–right dimension, but in this case delineates appeals to national sovereignty from challenges facing the institution as a whole. Consider that the two major agricultural parties within the corpus, the Centre parties in Sweden and Finland (C and Kesk, respectively), load most highly along this dimension, due to the role the EU plays in regulating agriculture and other environmental concerns.

Moreso than other latent dimensions presented so far, there are four clear quadrants within the EU topic. There is a right–wing, establishment quadrant emphasizing sovereignty and issues facing core member states, typiﬁed by the CDU in Germany. The left–wing, establishment is found in the bottom left corner, emphasizing Markets and International Summits. The right– wing, anti–establishment quadrant in the upper right emphasizes peripheral states and threats, with predominatantly nationalist parties, populist Liberals such as the Liberal People’s Party in Finland and Italy of Values in Italy, as well as reactionary regional parties such as the PNV and DUP in Spain and England, respectively. Finally, there is a left–wing, anti–establishment quadrant, which emphasizes voting, or an appeal to democratize European institutions, as well as marginalized Others and environmental subtopics.

3.6.2 Rhetorical Strategies

Beyond a particular substantive focus, populism is often characterized by certain rhetorical strategies. In an analysis of Belgian parties, Jagers and Walgrave (2007) ﬁnd negativity and hostility to be key features of right–wing, populist political communication. In order to capture this oppositional dimension, I turn to the rhetorical topics of Nonsense and Failure. In order to capture the anti–establishment orientation of populist rhetoric, I turn to the Representation

9The parties that served in government that scores most highly along this dimension are the Greens in Germany. 90 topic. This topic represents rejections of the political system as insufﬁciently accommodating to the interest of “The People” (Aalberg et al. 2016).

Rhetorical parent topics present a divergence from the previous discussion of substantive parent topics, necessitating a slight change in methodology and presentation. While previous subtopics are often particular issue areas or speciﬁc frames on a particular issue, there are no similar analogue for rhetorical topics. Instead, many subtopics are substantive dimensions within a particular rhetorical frame. For example, under the Failure rhetorical parent topic, nationalist parties might score highly on the EU dimension, implying that they frequently speak of that institution’s failures.

Instead of presenting keywords and individual subtopics overtime, which would be prohibitively long for three separate rhetorical subtopics, this section will present the SVD decomposition of each parent topic. These latent spaces of rhetorical competition are ideal for studying different patterns of parties’ political communication.

We begin the discussion with Figure 3.15, showing the decomposition for the Failure subtopic. The relative positioning of the parties is strikingly similar to Figure 3.14. The x–axis divides the parties between government and opposition, but could also be considered as pro– and anti– establishment and the y–axis maps somewhat onto a left–right cleavage, although Christian parties score in a similar direction to Social Democratic and Leftist parties. This is because the subtopics that load on the vertical dimension seem to be largely about general welfare: jobs; poverty; vulnerable groups; etc. frames that, as shown earlier, are generally the province of Christian parties among their right–wing brethren.

The main subtopic pointing in the anti–establishment direction is Communities. Some of the top keywords for this topic are “communities”, “people”, “workers”, and “you.” This is the appeal to the homogeneous group of “ordinary people” at the heart of populist discourse. Towards the Leftist side of this populist dimension, the subtopic with the highest loading is Poverty, 91

Weighted SVD of Mean Party Scores 0.3

● Ecological ● Left ● Social Dem. ● Liberal ● Christian Dem. Jobs ● Conservative ● Nationalist 0.2 uk_Lab ● Agrarian ● Ethnic/Regional Poverty it_SEL uk_Lab/Co−op nl_SP Vulnerableit_AP/NCD Scrutiny Tax Environment Healthcarefi_KD nl_GLfi_Vas 0.1 de_Linke sv_SAPLocal es_CiU es_IU nl_PvdA cs_KSCM uk_SNP Support sv_V fi_SDP it_PI/DES−CD fi_Kesk fi_Kokde_SPD Communities We cs_VV it_PD sv_KD Quotes it_MDP it_SC uk_LibDem Transit cs_Usvit nl_CUsv_C cs_TOP09 cs_CMUS cs_CSSD it_PT uk_Green it_UdCConstituents Figures Costses_PSOE Accountability Banks Education 0.0 Waitingde_CDU/CSU nl_CDA it_M5S it_P−UDEUR nl_D66Bills Elections sv_MP nl_SGP it_PRCDeficit fi_PS fi_Vihr nl_PvdD cs_KDU−CSL cs_ANOit_LN uk_Plaid it_FI/PdL cs_US sv_Msv_L es_PP Threats ~ Welfare it_AN/FdI nl_VVD cs_ODSde_Gruene cs_SPR−RSC it_PdCI sv_SD Impact sv_NyD es_CDS it_IdV es_PNV nl_PVV cs_ODA de_FDP −0.1 Evidence uk_DUP cs_KDS fi_Nuor it_FLI Crime EU it_DCA−NPSI fi_LKP uk_Con nl_LPF it_FdV it_RnP uk_SDLP −0.2 fi_RKP

uk_UKIP −0.3

−0.3 −0.2 −0.1 0.0 0.1 0.2 0.3 uk_Ind Lab Government ~ Opposition

Figure 3.15: Failure scaling 92 suggesting, concurrent with theoretical expectation, that Left–wing populists emphasize material appeals to ordinary people. In a mirror–image, Crime and the EU are the two topics most closely directed towards the right–wing populist dimension. These topics, consistent with the substantive dimensions already explored in this chapter, suggest that the right–wing populist appeal is grounded in appeals to voters’ fear and threats of outsiders to national cohesion.

Figure 3.16 replicates this process for the Nonsense parent topic. There is a similar forked pattern as the other figures in this section, with a forked pattern that has nationalist and leftist parties scoring similar scores along the horizontal dimension, but differing on the vertical dimension. The x–axis separates establishment and anti–establishment parties; with the establishment direction defined by the rhetorical frames Debate and Decisions, as well as substantive topics indicative of governing such as Military and Education. The other direction loads highly on threats or potentially negative topics revolving around Crime, Cost, and Conflict. The y–axis seems to divide parties between a focus on Welfare, in the negative direction, with topics such as Families and Healthcare, and security, with topics emphasizing Economic issues, crime and costs.

Two exceptions to the pattern shown in Figure 3.15 merit discussion. The UK Labour party is placed in a position similar to leftist, populist parties. This likely revolves around the UK’s position in opposing military conﬂict and austerity, placing it in a more populist rhetorical space. Second, the two mainstream, national Spanish parties PSOE and PP load in a similar direction to Nationalist parties, although not nearly as extreme. This positioning is best explained by the two parties’ emphasis of potential lawlessness and political violence by the various regional parties.

The left–wing populist quadrant is defined by discussion of elections, conflict, and families, showing that these subtopics are where claims of nonsense, or righteous indignation, are most clearly expressed. Not surprisingly, the right–wing populist quadrant is defined by a focus on Crime and Cost. These two frames evoke fears to personal safety as well as condemnations of a profligate establishment whose priorities are not in line with the people. 93

Weighted SVD of Mean Party Scores 0.3 cs_ANO ● Ecological ● Left cs_Usvit ● Social Dem. ● Liberal ● Christian Dem. it_LN Crime Cost it_IdV it_SC ● Conservative ● Nationalist 0.2 uk_SNPsv_NyD ● Agrarian cs_TOP09 ● Ethnic/Regional it_PT Economic Local es_CDSDebate sv_C EU it_UdC uk_Green fi_Kok 0.1 cs_VV cs_US fi_Kesk de_FDP it_AN/FdI uk_Lab/Co−op nl_VVD fi_Nuoruk_Con it_P−UDEUR it_M5S es_PP it_AP/NCDcs_CSSD de_SPDfi_SDPfi_Vihrsv_M nl_PvdDes_PSOE They uk_UKIP it_PI/DES−CD de_CDU/CSUsv_SAP nl_LPF cs_CMUSuk_LibDem nl_PVV fi_KD nl_PvdA fi_Vas sv_MP nl_CDA Military it_MDPnl_SP de_Gruenefi_PS nl_D66 0.0 es_CiU es_IU nl_GLcs_KSCMsv_KD Education it_FdV Parliament uk_Plaid nl_CU Welfare ~ Security Welfare Familiesit_SELsv_V fi_LKP sv_L Figures cs_ODS it_RnP uk_Ind Lab nl_SGP cs_KDU−CSL it_DCA−NPSI −0.1 uk_SDLP Decisions uk_Labcs_SPR−RSC it_PRCit_PdCI de_LinkeElections Healthcare it_PD uk_DUP

es_PNV

−0.2 it_FLI sv_SD

Conflict cs_ODA

fi_RKP

it_FI/PdL −0.3

−0.3 −0.2 −0.1 0.0 0.1 0.2 0.3

Costs ~ Decisions cs_KDS

Figure 3.16: Nonsense scaling 94

Weighted SVD of Mean Party Scores

it_M5S 0.3

● uk_SDLP Ecological ● Left ● Social Dem. EU ● Liberal ● Christian Dem. ● Conservative ● Nationalist 0.2 Representatives ● Agrarian sv_NyD it_AN/FdI ● Ethnic/Regional it_LN it_IdV

Results nl_VVD uk_Plaid es_CDS Violence it_FI/PdL uk_DUP cs_KDS uk_UKIP fi_PS 0.1 sv_MP es_PNV nl_CDA de_FDP cs_ODA it_MDP fi_LKP cs_ANOfi_Vas Elections nl_PvdA uk_SNP fi_Vihrcs_KDU−CSLnl_D66 nl_PVV cs_ODS Ballots uk_Con fi_SDP de_Gruene sv_M it_FdV cs_Usvit sv_V cs_CSSD it_PD it_DCA−NPSI LocalElectoral cs_TOP09 it_SEL de_CDU/CSU

0.0 fi_KDes_CiUfi_Kok sv_SD it_UdC sv_SAP Fraud cs_US nl_CU es_PPde_SPD uk_LibDem Registration nl_PvdD nl_SP Propositions sv_L it_PT it_RnP nl_SGP fi_Nuor es_PSOEcs_KSCMit_FLIcs_CMUSfi_Keskit_SC sv_C cs_SPR−RSCnl_GLde_Linke

Democracy ~ Representation Democracy es_IU −0.1

it_PdCI it_PRC

nl_LPF sv_KD

cs_VV it_PI/DES−CDit_AP/NCD

fi_RKP Boundaries uk_Green uk_Labit_P−UDEUR −0.2 Diversityuk_Lab/Co−op Democracy Development −0.3

−0.3 −0.2 −0.1 0.0 0.1 0.2 0.3

Opposition ~ Government

Figure 3.17: Representation scaling 95

Finally, Figure 3.17 presents the SVD decomposition of the Representation parent topic, which mainly focuses on elections and appeals for greater democracy. The x–axis of this ﬁgure again represents an opposition–government dynamic, while the y–axis delineates between calls for greater democracy and diversity and appeals to institutional representation, through either MPs or the EU. There is a similar spatial pattern where nationalist and leftist parties form two paths of a populist fork, aligned on the horizontal dimension, but separated across the vertical one.

Left–wing populists pay increased attention to Democracy, characterized as rhetorical appeals to broadened access to political decision making. In contrast, right–wing populists emphasize the EU and question whether or not parliament is truly representative of the polity’s wishes. This implies a different approach between these two populist wings: one which appeals to values of pluralism; and the other which castigates so-called “elite” institutions.

This section investigated the conceptually tangled populist dimension in parliamentary rhetoric, with particular attention to nationalist parties. Echoing other scholars, I ﬁnd that there are indeed left and right–wing dimensions to populist rhetoric. While both center around appeals to “ordinary people”, right–wing populists focus their appeal around two substantive issues: crime and the EU. The former represents an appeal to fears of personal safety whereas the latter represents appeals against threats from outsiders. This contrasts with left–wing populists who more emphasize individual deprivation.

3.7 Conclusion and Future Research

This paper has provided an overview of party competition within the ideological pillars at the core of nationalist parties’ political appeal: immigration; authoritarianism; and populism. With respect to immigration, I demonstrated how nationalist parties focus on a replacement narrative 96 that emphasizes increasing rates of immigration and pays particular attention to a perceived “over–foreignization” of society. For crime, I showed how nationalist parties adopt a similar ideological position as mainstream right–wing parties on issues of order or punishment, but attempt to outﬂank them by paying disproportionately more attention to individual crimes and offenses. Additionally, they are unique among other party families in portraying these crimes as public health issues, using language evocative of disease or contagion. Finally, I showed how nationalist parties frame populist appeals. While both left–wing and right–wing parties share an emphasis on “ordinary people” and their communities, right–wing populists frame their populist appeals as pushback against rampant crime and unrepresentative institutions, without the left– wing emphasis on democratic values.

The analysis presented herein offers many suggestions for future research. The most immediate avenue to explore is increasing the supply of available parliamentary speech. Unlike manifestos or expert surveys, parliamentary speech offers a unique opportunity to judge parties within their main arena of competition. Second, there exists considerable fruitful ground in showing how the strategic dynamics of parliamentary speech shape the expression of ideology. The analysis performed above could easily be replicated for environmental or liberal parties. Finally, while outside the scope of this project, the study of parliament speech must be merged with a discussion of external, contextual factors. This could answer critical questions of how parties respond to strategic incentives exogenous to internal party competition. 97

Chapter 4

The Radical Right in Competition

Under what conditions do establishment parties respond to new political foes? How will they attenuate or alter their agendas to respond to such threats? Following the continuing realignment of political cleavages around non–material axes, new parties have acted as issue entrepreneurs, opening up new dimensions for competition. Foremost among these issue entrepreneurs are radical right parties, often emphasizing a narrow agenda of xenophobia and cultural chauvinism. This paper provides new insight into how mainstream parties respond to these radical right challengers. It departs from past studies by using the content of parliamentary speeches themselves, providing a more direct measurement of how parties respond to shifting strategic incentives.

4.1 Issue entrepreneurs and responses from mainstream par-

ties

Beginning with the “third wave” of radical right parties ascending to power in the early-1980s, scholars have been focused on the sources of newfound support for extremist, xenophobic parties (Mudde 2013). Past studies have explained this rise in far–right support through contex- 98 tual factors, such as unemployment and immigration (Arzheimer 2009; Golder 2003; Jackman and Volpert 1996), a backlash to concessions granted to minority populations (Bustikova 2014; Ignazi 2002), or an individual party’s particular ideological appeals (Rovny 2013; Pellikaan, De Lange and Van der Meer 2007; Van der Brug, Fennema and Tillie 2000). Explanations that rest on voters’ incentives and preferences are termed “demand–side”, while those that focus on party competition and ideological appeals are considered “supply–side”.

While the previous chapter focused on the ideological appeals made by radical right parties, this chapter focuses on the response of other parties in parliament. Golder (2016) highlights the debate between those who argue that convergence or centrism among mainstream parties abets the radical right, with some studies ﬁnding evidence to support this position (Carter 2005; Van der Brug, Fennema and Tillie 2005; Abedi 2002); and others ﬁnding evidence to the contrary or no effect (Arzheimer and Carter 2006; Bustikova 2014; Norris et al. 2005). Rovny (2013) suggests that radical right parties deliberately obscure their position on economic issues, in order to emphasize the new issue areas (primarily immigration) that they wish to introduce.

Part of the literature that informs the approach to radical right parties revolves around issue ownership and salience Budge and Farlie (1978, 1983), which help to understand the debate around immigration, the primary issue associated with the radical right. This initial expansion of parliamentary competition beyond a Left–Right, spatial dimension to include questions of issue ownership and salience was further advanced by Meguid (2008), who argues that establishment parties have a choice between accommodative, adversarial, and dismissive strategies. While they cut across the ideological spectrum, smaller, “niche” parties have the following criteria in common: represent political cleavages divorced from class; introduce new issues and dimensions of competition; and are seen as single–issue parties (Meguid 2005). These new dimensions of competition follow the New Politics axes of competition, where environmentalism and cultural issues dominate an increasingly post–class discourse (Inglehart and Rabier 1986; Inglehart 1997). 99

Meyer and Miller (2015) argue that “nicheness” is actually a continuous concept, with the ideal niche party one that devotes all of its attention to a single issue totally neglected by establishment parties. Competitive issues emphasized by niche parties are different from mainstream parties. This impacts spatial competition; while mainstream parties are competing for the median voter within the entire electorate; niche parties are competing for the median voter only within their particular issue area (Budge, Ezrow and McDonald 2010). This could explain why niche parties tend to remain more consistent in their programmatic appeals (Adams et al. 2006). Bischof (2017) argues that, as issue entrepreneurs, niche parties are more likely to adopt extreme positions, but only because these areas might allow an opening for their issue of choice. Wagner (2012) ﬁnds that mainstream parties devote more attention to economic issues and have more heterogeneous ideological positions than niche parties. A consistent, and perhaps structural, theme of niche parties that transcends a typical ideological dimension is a focus on decentralization and increased representation (Taggart 1998; Heller 2002; Bomberg and Peterson 1998).

Many studies of niche parties use manifestos or expert coding to discuss these parties’ strategies and mainstream responses. Hix, Noury and Roland (2007) use the NOMINATE method for scaling roll call votes (Poole and Rosenthal 1985) in the European Parliament on two dimensions: a Left–Right economic split; and a pro–EU / anti–EU division. The positions that parties take in the European Parliament are mostly consistent with their national political priorities, which provides a useful proxy for actual positions taken in the national parliament (Hix 2002). Jensen and Spoon (2010), in a study of European Parliament roll-call votes, ﬁnd that niche parties have greater intra–party variation on economic issues, and emphasize decentralization more than other parties.

Building on the issue evolution model of Carmines and Stimson (1986), De Vries and Hobolt (2012b) distinguish three types of parties, each facing different strategic incentives: mainstream government parties, mainstream opposition parties, and challenger parties. It is the latter category of parties that pursue an issue entrepreneurship strategy, as mainstream parties are limited 100 by their need to maintain predictable behavior to better their coalition prospects (Green-Pedersen 2007). As pointed out by Meguid (2008), mainstream parties might choose to engage on new issues promoted by challengers if they believe strengthening the challenger party would be detrimental to their mainstream rivals. Green-Pedersen and Mortensen (2010); Green-Pedersen (2012) call these accomodative or adversarial strategies (i.e. direct responses, either pro or con) “politicization”, or the incorporation of a new issue into the space of mainstream discourse.

In his comparison of green and radical right parties, Abou-Chadi (2016) argues that mainstream parties will respond to the electoral success of radical right parties by shifting to anti–immigrant positions and emphasizing the issue of immigration. This is because they can contest the issue ownership of immigration through its politicization. Van Spanje (2010) also finds evidence that the success of radical right parties will shift mainstream parties, and particular conservative ones, towards anti–Immigrant positions. Han (2015) provides evidence to show the salience of immigration increases following the success of the radical right, but Akkerman (2015) cautions that this may be due to underlying, contextual factors. Further adding to the empirical confusion, Rooduijn, De Lange and Van der Brug (2014) find no evidence of a shift from mainstream parties, and Bale et al. (2010) find that parties’ responses to far-right entry are highly idiosyncratic, and difficult to generalize across countries.

Scholars have applied this “contagious politcs” theory to other issue areas besides immigration. In a study of welfare chauvinism, Schumacher and Van Kersbergen (2016) find that mainstream right–wing parties pursue an accommodative position towards radical right parties on this particular issue. Meijers (2017) finds that center–left parties demonstrate a stronger shift on the issue of Euroscepticism due to threats from both ends of the political spectrum. In a study of the Belgian parliament, Vliegenthart, Walgrave and Meppelink (2011) find evidence that mainstream parties politicize, that is, disproportionately discuss, both radical right and green party issues. While the research designs and cases under consideration differ, there does seem to be frequent findings of contagious politics, particular between the radical right and mainstream 101 center–right. However, the empirical record is not conclusive on this, and many point to a variety of potentially mediating or confounding factors.

4.1.1 Theoretical expectations

Much of the inconclusive evidence on contagious parties, or responses to issue entrepreneurs, relies on analyses of manifestos or expert coding. The data and method for cross–country analysis introduced in Chapter 2 provide a valuable new domain for these theories to be applied. As the primary battleground in party competition, parliamentary speeches provide an invaluable window into how parties respond to shifting strategic landscapes. Since parliamentary speech is a limited quantity, talk is not cheap, so we can assume that legislators are responding to strategic incentives and attempting to maximize their appeal to constituents or potential coalition partners.

From our previous discussion of parties’ responses to challengers, we can initially distinguish between three possible responses mainstream parties might have to ascendant radical right challengers and issue entrepreneurs:

1. Parties will pursue an accommodative strategy, moving closer to the issue entrepreneur’s position,

2. Parties will pursue an adversarial strategy, moving further away from the issue entrepreneur’s position, or

3. Parties will pursue a dismissive strategy, reducing the attention they pay to the issue put forward by the challenger party.

The ﬁrst two of these strategies, accommodative or adversarial, are examples of the “politicization” of an issue. Looking only at the topic level, it might be empirically difﬁcult to distinguish the two strategies. For example, a center–right party might increase its discussion of immigration to attempt to contest the issue ownership of radical right parties. Conversely, a center–left 102 party might devote more speaking time to immigration in order to castigate radical right parties. The former is intended to diffuse the threat of radical right parties, while the latter is to enhance it—for the rival mainstream parties, of course.

From our review of the previous literature, it is unlikely that we will see a consistent effect across all countries and parties. Instead, we should expect a mainstream party’s response to vary based on the following features of a parties position in parliament:

1. Whether the party is in opposition or government,

2. The party family or ideological position, particularly whether or not the party is center–left or center–right,

3. The threat or opportunity (for potential coalition purposes) posed by the challenger party, and

4. The type of issue being introduced.

First, whether or not a party is in government will have an effect on its response to challengers. Government parties could be better able to resist catering to new issues due to their greater control over the agenda and need to cover a broader range of topics. Second, the ideological orientation of a party, as well as its historical partners will impact its response. Speciﬁcally in the case of radical right parties, we might expect center–right parties to be more likely to accommodate the radical right on issues such as immigration, since their voting bases overlap. Third, whether or not a party is seen as a viable coalition partner will change how they present themselves in parliament. While many party systems have norms against including the radical right in government, this is not universally true, and, as in the case of Finland, subject to change. Finally, the type of issue itself matters, particularly whether or not it could conﬂict with the other main tenets of mainstream parties’ agendas, or the range of potential positions a party could take on a certain issue. 103

I depart from previous studies in not conﬁning this analysis to policy–oriented topics, a con- straint of programmatic–focused manifestos or second–hand expert evaluations. The speaking style, emotional appeals, framing, and presentation of issues is also important to voters and offers other axes of competition on which parties can compete (Esser and Pfetsch 2004). Fur- thermore, changing its rhetorical proﬁle provides a less expensive way for mainstream parties to counter challenger parties, as coalition partners are less likely to respond to rhetorical shifts that leave the substantive aspects of their coalition agreement intact. My use of parliamentary speeches allows me directly to address these questions.

As discussed, the question of contagious parties and mainstream response to challengers is not a new one. As will be shown, and as is hinted at in the literature, many of the ﬁndings are contingent on the particular characteristics of both the mainstream party that is responding to the challenge and the niche party making the challenge. This chapter focuses on parties’ response to the radical right. The general framework would apply to other niche parties, but differences in the issues and relationships with establishment parties might produce different strategies.

One unique contribution of this paper is the breadth of its topical scope. Many previous analyses are confined to a left–right dimension, or issue specific investigation. The approach outlined in this dissertation allows a comparison among the 78 substantive and rhetorical topics identified in the first chapter. This fine–grained topical space allows a more nuanced expansion of the strategies laid out by Meguid (2008). Specifically, it allows for what I will term

1.A soft adversarial strategy, where parties emphasize related issues without contesting the particular issue area presented by the challenger.

2. An oppositional strategy, where parties emphasize topics at the other end of an issue spectrum from the issue emphasized by the challenger.

The ﬁrst strategy, a soft adversarial approach, allows parties to counter issue entrepreneurs, rather than react to them. For instance, parties could emphasize discrimination or multicul- 104 turalism in response to radical right parties emphasis on immigration. This is deemed a “soft adversarial” approach, as it does not have the same politicization effect as a direct confrontation would. The latter strategy is another quasi-adversarial approach, but one that is more oblique. It seeks to counter issue entrepreneurs by politicizing rival ideas, not in an attempt to contest issue ownership or disprove party competence, but to turn that issue ownership into a disadvantage by emphasizing oppositional ideas.

Figure 4.1: Oppositional topics: capitol ∼ labor

Business Scores, United Kingdom 0.6 0.2 −0.2 Party−Year Scores Party−Year −0.6 1990 1995 2000 2005 2010 2015

year

Workers Scores, United Kingdom 0.6 0.2 −0.2 Party−Year Scores Party−Year −0.6 1990 1995 2000 2005 2010 2015

year 105

Figure 4.2: Oppositional topics: immigration ∼ environment

Immigration Scores, Italy 0.6 0.2 −0.2 Party−Year Scores Party−Year −0.6 1990 1995 2000 2005 2010 2015

year

Environment Scores, Italy 0.6 0.2 −0.2 Party−Year Scores Party−Year −0.6 1990 1995 2000 2005 2010 2015

year

An oppositional topic is a pair of topics that represent two poles of a dimension in political semantic space. Figure 4.1 shows a classic example of oppositional topics using the Conservatives and Labour in the UK: capitol, represented by the topic Business, against labor, represented by the worker topic. The use of an oppositional topic can accentuate the particular dimension in question, by increasing the salience of both topics in tandem. Figure 4.2 provides an example 106 of oppositional topics along a “New Politics” dimension, pitting environmental issues against immigration, exempliﬁed here by Lega Nord, along with their close allies the Brothers of Italy, against the Five Star Movement.

So far, I have summarized the possible strategies that can be pursued by mainstream parties in response to radical–right entry. These include dismissive, accommodative, and adversarial strategies. I have also distinguished between different type of more nuanced adversarial strategies: weak adversarial strategies that emphasize adjacent topics, and oppositional strategies that emphasize the other pole of contentious issues. This leads to the following theoretical expectations:

H1: Center–right parties will pursue a dismissive strategy towards radical–right parties.

H2a: Center–left parties will pursue adversarial strategies, preferring direct confrontation when they are in opposition and resorting to weaker adversarial and oppositional strategies when they are in government.

H2b: When pursuing adversarial strategies, center–left parties will attenuate their attention to left–wing economic issues

The ﬁrst hypothesis, that center–right parties will pursue dismissive strategies, stems from the threat to their electoral base that radical–right parties pose. While there have been some ﬁndings in the literature that suggest center–right parties pursue accommodative strategies, I suggest this should only occur when radical–right parties present a strong threat to center–right parties. Throughout the years and countries under study here, the radical–right does not often have many seats in parliament; therefore center–right parties are better off preventing the politicization of radical–right issues by adopting a dismissive strategy.

Hypothesis two deals with center–left parties. Meguid (2008) ﬁnds that center–left parties will pursue an adversarial strategy in order to weaken their center–right opponents by boosting the 107 salience of radical–right issues. This theory is encompassed in H2a. However, unlike Meguid (2008), who argues that competition will occur only on the new issue dimension, I argue that center–left parties will devote less attention to traditional left–right issues. The logic behind this is similar to the logic behind center–left parties adopting adversarial strategies. Adversarial strategies work by increasing politcization of radical–right issues, to the detriment of center– right parties. De–emphasizing competing dimensions of competition, such as the traditional left–right economic split, also increases the salience of new axes of competition by reducing the salience of old ones. Additionally, it reduces the ideological cost of voters switching form center–right to center–left parties if they are alienated by center–right parties potentially accommodating radical–right parties.

The following sections illustrate these strategic dynamics within the corpus introduced in chapter one. Germany and Spain are both excluded from the analysis since neither had a nationalist party seated in the legislature during the time covered by the ParlSpeech data. The topical space is the same as identiﬁed in Chapter 1, and document scoring procedures remain the same.

4.2 Issue attention and ownership

Before discussing how mainstream parties respond to radical right challengers, it is important to define the issue space, and stipulate which parties devote greater attention to which issues. When presenting these, I avoid wading into potentially difficult classification issues around what con- stitutes the “center–left” and “center–right”, by using the Comparative Manifestos classification of party families. Specifically, I operationalized “radical–right parties” as those categorized by CMP as “Nationalist” and “mainstream parties” as those categorized by CMP as “Conservative”, “Social Democratic”, “Christian Democratic”, and “Liberal.”

Figure 4.3 presents the difference in topic attention between radical right parties and all others 108

Figure 4.3: Relative attention for Nationalist parties compared to others

NAT vs other parties

Representation ● Immigration ● #Failure ● #Nonsense ● Crime ● Religion ● OECD/Trade ● Budget ● #Quotes ● #Disaster ● #Skepticism ● International Crises ● Maritime ● Professions ● Public Health ● Prisons ● Inequality ● Europe ● #I_am_ ● #My_ ● Local/Regional ● Housing ● Defense ● #Standards ● Bureaucracy ● Courts/Constitutional ● #Uncertainty ● #Consequences ● #Costs ● Transport ● Agriculture ● #Transparency ● #Comparisons ● Energy ● Sport ● Pensions ● Telecom ● Macroeconomy ● Media ● Rights ● #Decisions ● #Groups ● Banks ● #Praise ● #Questions/Answers ● History/Heritage ● #Procurement ● #Compliance ● Taxes ● #Issues ● Terrorism ● #Reasons ● Private/Public ● Workers ● Business ● Disabilities ● #Problems/Solutions ● Agencies/Bodies ● Universities ● #Statistics ● Jobs ● Science/R&D ● Education ● Families ● #Change ● #Rules ● #Timetable ● Agreements ● #Deliberations ● #Objectives ● #Alternatives ● Health ● #Initiatives ● Discrimination ● Global (Aid/Climate) ● #Studies ● Environment ● #Differences ●

−0.05 0.00 0.05 0.10

Less attention ~ More attention 109 in parliament. Note that this ﬁgure, as the ones to follow, represent relative attention: greater numbers imply that radical right parties pay more attention to a topic in comparison to an average for all other parties. A relative understanding of attention to issues is clearly justiﬁed. It guards against temporally based confounding (“history threats”) or other external shocks that might shift the debate to a certain issue (unless those are unevenly mediated depending on a particular party’s characteristics), and captures the competitive nature of political positioning. In other words, it is not important how much attention a party pays to an issue, only how that level of attention differs from their competitors.

As can be seen from this ﬁgure, the two issues that radical right parties devote more attention to than other party families are Representation and Immigration.1 An emphasis on Immigration is consistent with radical–right parties status as niche parties and issue entrepreneurs for anti– immigrant policies. An increased focus on representation is also to be expected. Not only do nationalist parties, like other niche parties, consistently advocate for a greater say in largely exclusionary institutions, but nationalist parties, unlike other niche parties, are consistently anti– European, and make the lack of a democratic voice in that institution a major feature of their campaigns. #Failure and #Nonsense are both rhetorical topics, often associated with opposition parties. They convey particularly negative and dismissive assessments of the government’s performance. Due to the overt anger and hostility presented by nationalist parties, it is to be expected that these rhetorical strategies are employed at a greater rate even when compared to other opposition parties. Additionally, Crime and Religion, the latter of which encompasses references to Islam, appeal to the authoritarian and nativistic tenets of nationalist ideology.

Of equal importance are the issues that radical right parties pay comparatively less attention to. These include the substantive topics of the Environment, Discrimination, and Global (Aid/Climate). As shown in the ﬁrst chapter of this dissertation, environmental concerns lie at the opposite end of the “New Politics” axis from nationalist issues. Similarly, discussion of

1Specific topics are capitalized to denote that it is referring to one of the topics identified in Chapter 1, rather than the colloquial meaning. 110 discrimination is difficult to reconcile with an openly chauvinistic and parochial ideology. The rhetorical topics that the radical right emphasizes far less than other parties are #Differences and #Studies. The former implies a collegial atmosphere, whereas radical right appeals are grounded in the righteousness of the people and the wickedness of the establishment. Due to their opposition to technocracy and disdain for the establishment, it is also not a surprise that radical right parties do not cite #Studies or make empirical evidence a large rhetorical component of their appeal.

Figure 4.4 shows a condensed version of the other mainstream party families and their relative differences with all other parties. Conservative parties focus mostly on supply–side economic issues: Business, Taxes, and Macroeconomy. Conservative parties also devote the least attention to Inequality, along with signiﬁcantly less attention to environmental concerns such as Energy, Environment, and Global (Aid/Climate). Social Democratic parties, on the other hand, emphasize labor concerns and question the fairness of the global economic system, disproportionately emphasizing Workers, Jobs, Banks, and Inequality. Rhetori- cally, Social Democratic parties favor discussion of #Initiatives and #Timetables for projects.

According to the lower dimensional representation of the topic space presented in Chapter 1, Liberal parties were the most heterogeneous, and while many take part in governing coalitions, they hold fewer seats than Conservative or Social Democratic parties. Nevertheless, clear patterns emerge from their relative attention to topics. Liberal parties focus disproportionately on Media, Courts, and Representation, reﬂecting their interest in rights and freedoms. In line with their predominantly right–wing economic orientation, they also share a relatively increased focus on Business and Taxes. Also indicative of this free market economic pos- ture, the topic they pay the least attention to is Inequality. Christian Democratic parties, while more homogeneous in their ideological position than Liberals, share a wide variation in the extent that each constituent party in the family can be termed mainstream. As to be ex- 111

Figure 4.4: Issue attention by party family, mainstream parties

CON vs other parties SOC vs other parties

Business ● Workers ● Taxes ● #Initiatives ● Macroeconomy ● #Timetable ● #Nonsense ● Jobs ● #Praise ● Banks ● Universities ● Inequality ● Bureaucracy ● Local/Regional ● #Costs ● Universities ● Crime ● #Statistics ● Agencies/Bodies ● Education ● Representation ● Agriculture ● International Crises ● Taxes ● #Groups ● History/Heritage ● Energy ● Courts/Constitutional ● Environment ● Religion ● Discrimination ● #Costs ● Global (Aid/Climate) ● Rights ● #Skepticism ● #Skepticism ● #Failure ● Representation ● Inequality ● #Failure ●

−0.05 0.00 0.05 −0.05 0.00 0.05

Less attention ~ More attention Less attention ~ More attention

LIB vs other parties CHR vs other parties

Media ● Families ● Courts/Constitutional ● Rights ● Agencies/Bodies ● Religion ● Business ● Health ● #Change ● #Reasons ● Representation ● Disabilities ● Taxes ● #Rules ● #Transparency ● #Uncertainty ● #Rules ● #Praise ● Universities ● #Initiatives ● Disabilities ● Transport ● #Standards ● #Transparency ● Local/Regional ● Representation ● Workers ● Banks ● #Disaster ● Courts/Constitutional ● #Failure ● #Failure ● Environment ● Inequality ● Families ● Workers ● Agriculture ● #Nonsense ● Inequality ● Energy ●

−0.06 −0.04 −0.02 0.00 0.02 0.04 −0.05 0.00 0.05 0.10

Less attention ~ More attention Less attention ~ More attention

Only top ten and bottom ten topics listed. 112 pected, they focus predominantly on values, paying signiﬁcantly more attention to topics such as Families, Rights, and Religion. All of the party families share a comparative lack of attention to the #Failure topic, reﬂecting their greater likelihood of serving in government.

Figure 4.5: Issue attention by party family, challenger parties

LEF vs other parties ECO vs other parties

Workers ● Energy ● Inequality ● Environment ● #Failure ● Global (Aid/Climate) ● International Crises ● #Failure ● Discrimination ● Agriculture ● Housing ● Science/R&D ● #Standards ● #Skepticism ● Banks ● #Studies ● Jobs ● #Nonsense ● Private/Public ● Representation ● Bureaucracy ● #Procurement ● Agreements ● #Standards ● Sport ● #Issues ● Agencies/Bodies ● Taxes ● Agriculture ● Professions ● #Rules ● Workers ● #Timetable ● Agencies/Bodies ● #Initiatives ● Business ● #Praise ● Health ● Business ● Local/Regional ●

−0.05 0.00 0.05 0.10 0.15 −0.10 −0.05 0.00 0.05 0.10 0.15 0.20

Less attention ~ More attention Less attention ~ More attention

Only top ten and bottom ten topics listed.

Turning to other challenger parties, Figure 4.5 shows relative attention to topics for members of the Leftist and Ecological party families. The ﬁrst aspect to note is the scale of the x–axis: while the mainstream parties have certain issues they emphasize more than others, the values for Leftist and Green parties are very large, conveying their status as niche parties with a more narrow ideological agenda. The distribution is also asymmetrical, unlike most of the mainstream parties. While conservatives avoid discussing inequality at relatively the same level as they prioritize discussions of business, Leftist parties talk about Workers and Inequality far more than they avoid talking about Business.

The topics that each of these party families pay attention to conform to theoretical expectations. 113

Greens talk the most about Energy, Environment, and Global (Aid/Climate), consistent with their ecological focus. They also avoid discussions of Local/Regional, reﬂect- ing their international orientation and dispersed constituency. Additionally, they disproportionately discuss economic issues less, including those favored by the right, such as Business and Taxes, and those favored by the left, such as Workers. Leftist parties disproportionately emphasize economic issues: focusing on topics often discussed by Social Democratic parties such as Workers, Banks, Jobs, and Inequality. Unlike Social Democratic issues, they also emphasize cultural and international dimensions such as Discrimination and International Crises. Reﬂecting their consistent status as part of the opposition, both parties disproportionately employ the #Failure rhetorical strategy.

4.3 Impact of radical right parties

There are numerous ways to operationalize which speciﬁc stimulus mainstream parties respond to: is it increased public support for radical right parties; a proportional function of the radical right’s electoral results; perhaps even international trends, the results in other elections, or the results in the European Parliament. It is likely that all of these provoke a response in mainstream parties. However, it is theoretically unclear that any of these stimuli might provoke a different or unique response. Indeed, all are manifestations of the radical right’s growing success and its challenge to mainstream parties. Therefore, this section focuses on the operationalization that provides the most analytical leverage: the presence of radical right parties in parliament.2

2Results obtained from a continuous operationalization are contained in the appendix, and don’t differ substan- tially (although, due to the increased variation, they are less precise). 114

4.3.1 All parties

Before investigating the disaggregated affect of radical right parties in regards to government status or ideological positioning, we can ﬁrst focus on the general effects of radical right entry into parliament. Figure 4.6 illustrates the relative attention paid to each topic by mainstream parties when radical right parties are or are not seated in the legislature. A positive value indicates that more attention is paid to a particular topic when radical right parties are present, and a negative value indicates that comparatively less attention is paid to a topic when the radical right is present. Topics which are emphasized more by mainstream parties when radical right parties are seated are in orange, and topics emphasized less with radical right parties present are in black.

Note that the figure includes mainstream parties, irrespective of their ideological positioning, which might muddle the results. Other challenger parties are excluded, because they do not face any threat from radical right parties (insofar as they have little to lose), and so are bound by a different set of incentives. The evidence does not suggest a universal strategy adopted by mainstream parties, but does provide some clues. First, Immigration is consistently de– emphasized when nationalist parties are present. This points to a dismissive strategy by attempting to avoid the politicization of this issue. While there is not clear evidence of an adversarial strategy, it does appear that some of the topics more consistently emphasized when nationalist parties are present do appear to be a rebuke of their agenda: specifically, increased discussion of Rights, Discrimination, and Religion. This could be indicative of a “soft adversarial” approach, where parties argue against the xenophobic attitudes of radical right parties, not by directly contesting them on the issue of immigration, but by emphasizing liberal values and discrimination. On the other side, it does seem left–wing topics, or those associated with Social Democratic parties, such as Workers, Jobs, and Banks are de–emphasized. This finding will be discussed in further depth when charting the response of left–wing parties specifically. 115

Figure 4.6: Attention to topics for mainstream parties by presence of radical–right parties

Mainstream parties (CON, SOC, LIB, CHR)

Education ● Banks ● Immigration ● Jobs ● Universities ● Workers ● #Standards ● Health ● Global (Aid/Climate) ● Private/Public ● Religion ● Discrimination ● Rights ● Agencies/Bodies ● Science/R&D ● Prisons ● International Crises ● Representation ● Sport ● Agriculture ●

−0.02 −0.01 0.00 0.01 0.02 0.03

Less attention ~ More attention

Each point represents the difference between attention to topics with and without nationalists present in the legislature for all four mainstream parties (Conservatives, Social Democrats, Liberals, and Christian-Democrats). Larger values indicate more attention is paid to a topic when radical right parties are present, while smaller values indicate less attention. 116

4.3.2 Disaggregated Effects

While interesting, it is likely that the effects of the radical right in parliament shown in the previous graph are an amalgamation of a variety of different responses depending on the particular characteristics of the party responding. This would agree with the literature, which suggests there needs to be a disaggregation of parties in order to better understand the strategic incentives facing parties in different positions.

First, we look at the parties’ responses to far–right entry by party family. For parties on the right of the traditional left–right cleavage, radical right parties pose a threat to encroach on their voting share. Parties on the center–left, while they might face some competition for voters, they also have a potential incentive to boost radical right parties in order to diminish their opponents (Meguid 2008).

Figure 4.7: Relative attention to topics, by radical–right seated

Conservative Social Democratic

Crime ● Universities ● Transport ● Workers ● Immigration ● Jobs ● Telecom ● Education ● Energy ● #Praise ● Business ● #Deliberations ● Environment ● #Timetable ● #I_am_ ● #Consequences ● Families ● #Problems/Solutions ● Europe ● Global (Aid/Climate) ● #Differences ● Defense ● Maritime ● Telecom ● Religion ● Energy ● #Procurement ● Courts/Constitutional ● #Consequences ● Sport ● History/Heritage ● #Failure ● Health ● #Uncertainty ● Education ● #Nonsense ● #Studies ● #Skepticism ● Prisons ● Agriculture ●

−0.10 −0.05 0.00 0.05 0.10 −0.10 −0.05 0.00 0.05 0.10

Less attention ~ More attention Less attention ~ More attention

We begin our discussion of differential effects on party families by looking at Conservatives, the 117 group most likely to lose out to an insurgent radical right. Figure 4.7 shows the differences in relative attention among conservative parties when nationalist parties are seated in the legislature. This provides clear evidence of a dismissive strategy pursued by conservative parties. Two of the most salient issues for the radical right, Crime and Immigration are under-emphasized. The most disproportionately emphasized topic is Prisons. This dichotomy, with Crime as the least disproportionately emphasized topic and Prisons as the most disproportionately emphasized topic is reminiscent of the investigation of authoritarian ideological positioning in the previous chapter. In that chapter, I found that conservative and other traditional right–wing parties place a greater emphasis on Law and Order, whereas radical right parties emphasize particular crimes and violent offenses in order to inculcate a sense of fear.

The other portion of Figure 4.7 shows the response of Social Democratic parties to a radical right presence in the legislature. On the positive side, the most clear result is an overemphasis on negative rhetorical frames, such as #Uncertainty, #Nonsense, and #Skepticism. This lends credence to the “soft adversarial” strategy previously described. Social Democratic parties also devote less attention to the core left–wing issues of Workers or Labor. This is consistent with a strategy of encouraging radical right support in order to alienate potential right–wing voters. By encouraging these reckless parties and downplaying their ideological commitments, Social Democratic parties offer an alternative to right–wing voters for whom the presence of the radical right sours their perspective on the entire right–wing of the political spectrum.

Unlike Social Democratic or Conservative parties, which are almost always part of mainstream coalitions, Liberals and Christian–Democrats are more heterogenous in the extent to which they present as viable governing parties. This makes the threat posed by radical right parties unclear: for some, such as the CDA in the Netherlands, the radical right could pose a strong threat, whereas a Liberal party such as the populist, non–governing Italy of Values would face very different strategic incentives. According to Figure 4.8, Liberal parties seem to offer a similar 118

Figure 4.8: Relative attention to topics, by radical–right seated (Liberal and Christian Democrat)

Liberal Christian Democratic

#Standards ● Education ● Health ● Banks ● Inequality ● Health ● Workers ● #Nonsense ● #Nonsense ● Environment ● #Timetable ● Global (Aid/Climate) ● #Deliberations ● #Comparisons ● Transport ● #Failure ● Immigration ● Universities ● #Questions & Answers ● Maritime ● Crime ● #Issues ● #Studies ● Science/R&D ● #Objectives ● #Deliberations ● Representation ● Rights ● #Initiatives ● Local/Regional ● Universities ● Macroeconomy ● Sport ● Agencies/Bodies ● Environment ● Crime ● #Praise ● Discrimination ● Agriculture ● International Crises ●

−0.10 −0.05 0.00 0.05 0.10 −0.10 −0.05 0.00 0.05 0.10

Less attention ~ More attention Less attention ~ More attention response to radical right parties similar to Social Democrats. They emphasize ecological issues such as Environment and Agriculture, both of which are strongly de–emphasized by radical right parties. This is an example of an oppositional adversarial approach which emphasizes competing issues; in this case the opposite end of the “New Politics” axis. Additionally, like Social Democratic parties, they downplay ideologically left issues such as Inequality or Workers.

Christian Democratic parties, while as diverse as Liberal parties in the extent to which they form large governing parties, are more ideologically coherent. Figure 4.8 provides clear evidence of an adversarial approach adopted by Christian parties. They share the soft adversarial emphasis on Rights and Discrimination, while directly confronting radical right parties on issue areas of emphasis such as Crime and International Crises. This willingness to politicize issues associated with the radical right could reﬂect Christian parties’ more marginal state 119 in parliament, which gives them less to lose from potential challengers. This also echoes the conclusion found in the previous chapter, where Christian parties were engaged in more direct confrontation with radical right parties, as both are competing to be the primary alternatives to Conservatives on the right.

Figure 4.9: Relative attention to topics, by radical–right seated (Leftist and Ecological)

Leftist Ecological

Macroeconomy ● Families ● Workers ● #Rules ● Jobs ● #Initiatives ● #Problems/Solutions ● Health ● Transport ● Disabilities ● International Crises ● Agriculture ● #Praise ● Bureaucracy ● Global (Aid/Climate) ● #Timetable ● Energy ● Local/Regional ● #Disaster ● #Statistics ● Banks ● Media ● #Timetable ● Jobs ● Agreements ● #Reasons ● #Rules ● #Nonsense ● Religion ● #Costs ● Agencies/Bodies ● Inequality ● #Procurement ● #Uncertainty ● Education ● #Skepticism ● #Deliberations ● Workers ● Environment ● #Failure ●

−0.10 −0.05 0.00 0.05 0.10 −0.10 −0.05 0.00 0.05 0.10

Less attention ~ More attention Less attention ~ More attention

While very rarely parts of governing coalitions, there is no reason to expect that other perennial challenger parties, such as Leftists and Green parties would not shift their behavior in parliament to adjust for radical right entry. They do, however, face a different incentive structure. Both are angling to be minority parties in a center–left government, and neither are competing for the same share of the electorate as radical right parties, with some exceptions for populist appeals by Leftist parties.

As shown in Figure 4.9, Leftist parties follow a similar pattern to Social Democratic parties in their de–emphasis of core left–wing issues like Workers and Jobs. This is the exact opposite 120 of the pattern found among Ecological parties, which is the only party family that emphasizes economically left–wing topics like Workers, Jobs, and Inequality. This implies a convergence of broad left–wing party families: Leftists and Social Democrats de–emphasize traditional left–wing issues while Green parties emphasize them. Recalling the spatial conﬁguration of parties presented in the ﬁrst chapter, this suggests a reorientation of axes of competition, with a broad coalition of left–wing parties shifting more towards the “New Politics” dimension by de–emphasizing left–wing economic appeals.

So far, this chapter has found evidence that Conservative parties are more likely to adopt dismissive strategies in response to radical right parties, by devoting relatively less attention to topics owned by radical right parties, such as Crime, Immigration, and Religion. On the other hand, Social Democratic parties have two separate responses. First, they adopt a soft adversarial approach, that reframes radical topics on more advantageous ideological ground (for example, by emphasizing Discrimination in response to the radical right’s emphasis on Immigration). Second, they de–emphasize left–wing economic issues, such as Workers or Jobs. Adversarial approaches are not costly, as the politicization of radical right issues only serves to weaken their opponents. However, the evidence suggests that the weakening is not accomplished solely through boosting the radical right’s electoral prospects—while there are often norms against coalition formation with radical right parties, if these were to be violated there is potential for the politicization of radical right issues to backﬁre if they lead to an increased vote share of the right–wing overall and a coalition between mainstream center–right parties and the radical right. By de–emphasizing traditional left–wing economic issues, Social Demo- cratic parties broaden their appeal to centrist voters, guarding against a potential center–right / radical–right coalition, by setting themselves up to capture voters who might be alienated by the rightward shift that such a coalition would entail.

To investigate this further, Figure 4.10 provides relative differences in topic attention when nationalist parties are represented in parliament for both Social Democratic parties in opposition 121

Figure 4.10: Relative attention to topics, by radical–right seated (Social–Democrats / Opposition — Government)

Social Democrats, in government Social Democrats, in opposition

Universities ● Workers ● Education ● Macroeconomy ● Jobs ● Inequality ● #Problems/Solutions ● Pensions ● Workers ● Universities ● #Initiatives ● Jobs ● #Issues ● #Failure ● Agencies/Bodies ● Local/Regional ● #Costs ● #Statistics ● #Change ● Private/Public ● Representation ● Immigration ● #Skepticism ● Religion ● Maritime ● Courts/Constitutional ● Inequality ● Prisons ● #Nonsense ● Agencies/Bodies ● Defense ● Public Health ● #Uncertainty ● Environment ● #Failure ● Agreements ● Energy ● Discrimination ● Agriculture ● #Rules ●

−0.15 −0.10 −0.05 0.00 0.05 0.10 0.15 −0.15 −0.05 0.00 0.05 0.10 0.15

Less attention ~ More attention Less attention ~ More attention and those in government. It is clear that the adversarial strategy described above is most evident for Social Democratic parties in opposition. This comports with theoretical expectation: governing parties are less likely to alter their strategic appeal when they have already won. So- cial Democratic parties in opposition pay disproportionately less attention to economic issues, including ones central to left–wing appeals like Workers, Jobs, and Inequality. Con- versely, they devote a disproportionate amount of attention to topics such as Discrimination and Environment, which represent oppositional attacks on radical right positions, as well as direct adversarial approaches by discussing topics frequently referenced by radical–right parties such as Religion and Immigration. Social Democratic parties in government, share the centripetal strategy of de–emphasizing economic issues (although not to the same extent as those in opposition). However, there is no evidence of direct confrontation with the radical right on their issue areas of choice. Instead, Social Democratic parties in government empha- 122 size Environment and Agriculture, positioning them on the opposite end of the “New Politics” axes to radical–right parties.

Figure 4.11: Relative attention to topics, by radical–right seated (Conservatives / Opposition — Government)

Conservatives, in government Conservatives, in opposition

Global (Aid/Climate) ● Business ● Energy ● Local/Regional ● #Skepticism ● Transport ● Immigration ● Environment ● Courts/Constitutional ● Agriculture ● #Nonsense ● Crime ● Crime ● #Disaster ● #Costs ● #Change ● Representation ● Telecom ● Families ● #Standards ● #Consequences ● Maritime ● #Rules ● Global (Aid/Climate) ● #Procurement ● #Differences ● Housing ● #Comparisons ● #Issues ● Prisons ● Agencies/Bodies ● #Skepticism ● History/Heritage ● Rights ● Sport ● #Studies ● Local/Regional ● Representation ● Agriculture ● #Nonsense ●

−0.10 −0.05 0.00 0.05 0.10 −0.10 −0.05 0.00 0.05 0.10

Less attention~ More attention Less attention ~ More attention

Figure 4.11 provides the same distinction between opposition and government parties for those in the Conservative party family. A dismissive strategy is far more evident for Conservative parties in government, who devote relatively less time to discussing Immigration or Crime. Additionally, this dismissive strategy seems to apply to the entire New Politics axes, with Con- servative parties in government also disfavoring discussing of Global (Aid/Climate). Conservative parties spend a disproportionate amount of time emphasizing Local/Regional and Agricultural topics, suggesting that they are attempting to reach out to rural voters who they might be at risk of losing to radical right parties, on an issue terrain that is more favorable to them. When looking at Conservative parties in opposition, evidence for a dismissive strategy is much more limited, as Crime is the only disproportionately right–wing issue 123 that is de–emphasized. However, there is some evidence that Conservative parties in opposition pursue a limited accommodative strategy by paying disproportionate attention to #Nonsense and Representation, two topics associated with populist appeals.3 However, this seems to be largely a rhetorical shift, as there is no evidence of accommodation on the core substantive appeals of the radical right, such as Crime, Immigration, and Religion.

4.4 Conclusion

This chapter has investigated the reaction of other parties to the radical right in the Czech Repub- lic, the Netherlands, Italy, Finland, Sweden, and the United Kingdom. To my knowledge, this is the ﬁrst such comparative test using actual text from parliamentary speeches in a cross–national setting. While the empirical record is slightly unclear, this study deviates from the majority of the literature by ﬁnding little evidence of accommodative approaches from Conservative or other Center–right parties. This does not necessarily imply other studies are incorrect, as the incentives governing parliamentary speech could be fundamentally different from those governing than manifestos or single issue ratings.

Instead, this study finds considerable evidence of adversarial strategies pursued by the center– left. Specifically, the fine–grained topic space produced in Chapter 2 allows for a nuanced understanding of how adversarial strategies are employed, showing evidence of a soft adversarial approach, which emphasizes adjacent issues that can counter the issues introduced by challengers, as well as oppositional strategies which emphasize spatially opposed issued on different axes of political competition. In addition to these adversarial approaches, this study also found that left–wing parties, in general, tend to de–emphasize traditional left–right economic issues. I have argued that this is complementary to the strategy of an adversarial approach, by turning the politicization of a new issue into a liability for a party’s opponents, and altering the

3See chapter 2. 124 party’s appeals to accommodate the new opportunities this politicization might create.

One area in which the literature is consistent on is that party strategy is highly contingent on the particulars of a party or a country’s political system. I am therefore hesitant to suggest that the ﬁndings here be interpreted beyond the six countries under study. However, this study does show the importance of new data sources and particular those that encompass political speech itself in order to better understand how parties react to strategic incentives. Additionally, I show that an expansion of the issue space under consideration and increasing the theoretical dimensions on which parties compete allows for a more nuanced understanding of interparty competition. Further expanding the scope of the data would allow an even richer understanding of party competition. 125

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Appendices 142

Appendix A

Chapter 2

A.1 Substantive Keywords

The following tables display the top 20 loading keywords from substantive topics in all languages. The topics are presented in no particular order.

Table A.1: Pensions, Terrorism, Representation, and Professions keywords

Pensions Terrorism Representation Professions it pensione de Terrorismus es elecciones it professionali de Rente it po it elezioni it professionalita` de Rentenversicherung it terrorismo en election it professione en pensions en terrorism nl verkiezingen it professionale en pension sv terrorism de Wahler¨ en professional en retirement en terrorist de Wahl sv kompetens de Renten nl AIVD es electoral it professioni fi elakkeelle¨ nl terroristische it elettorale sv sjukskoterskor¨ es pensiones it ndrangheta de Wahlen de Qualifikation sv pension en threat fi vaaleissa nl deskundigheid it previdenziale de Anschlage¨ de Wahlrecht en qualified de Altersversorgung en intelligence de Wahlerinnen¨ nl beroepsgroep nl slot de Sicherheitsbehorden¨ en elections en profession sv pensionssystemet sv terrorismen nl kiezers de Ausbildung de Rentenreform en terrorists es electorales es carrera nl pensioen de Verfassungsschutz sv valet es profesion´ nl pensioenen sv terrorister sv val en trained de demographischen nl dreiging it elettori nl vaardigheden sv pensionssystem sv Svar sv valjare¨ en nurses de Altersarmut nl aanslagen fi vaalit nl opleiding 143

Table A.2: Inequality, Discrimination, Health, and Agriculture keywords

Inequality Discrimination Health Agriculture de sozial it donne de Gesundheitspolitik de Bauern de Gerechtigkeit de Diskriminierung de Arzte¨ de Landwirtschaft sv klyftor fi tasa de Pharmaindustrie it agricoltura sv klyftorna es hombres de Patienten en farmers sv rattvisa¨ it parita` de Arzten¨ es Agricultura de soziale de Frauen de Zoller¨ it agricoltori it deboli de Gleichstellung nl Oudkerk sv jordbrukspolitiken sv rikaste en men cs zdravotnictv´ı en agricultural de sozialer nl mannen de Krankenhaus de Landwirte sv rattvist¨ en discrimination de Gesundheitswesen sv lantbrukare sv fordelning¨ nl vrouwen nl geneesmiddelen en farming sv samst¨ en equality de Therapie fi maatalouden de sozialen es igualdad es Sanidad sv naringen¨ it poveri es mujer en doctor sv jordbrukspolitik sv jamlikhet¨ de Gleichberechtigung sv patienten de landwirtschaftlichen it ricchi nl gelijke it sanitario de Landwirten en poorest fi naisten en medical cs zemedˇ elstvˇ ´ı sv rika sv kvinnliga sv lakarna¨ en agriculture sv fordelningspolitik¨ es mujeres es medicamento it agricole sv solidaritet en women cs leka´ riˇ sv jordbruket 144

Table A.3: Envrionment, International Crises, Private / Public, and Banks keywords

Environment International Crises Private/Public Banks de Naturschutz it Siria de offentlichen¨ es accionistas es ambiental nl Egypte de offentliche¨ cs republika sv miljobalken¨ es Oriente en private de Anleger de Umwelt de Regime it privato en Rock de Natur en Zimbabwe de privaten en shareholders en environmental de Sudan de private de Banken en Natural es Cuba sv sektor de Bank fi luonnon nl mensenrechten sv sektorn it risparmiatori cs prostredˇ ´ı de Kolumbien en public sv Overl¨ aggningen¨ en wildlife de Region es privado it Banca de Flachen¨ de Irak de Daseinsvorsorge nl aandelen es ambiente de Iran sv privata it soci es medioambiental it Russia es publico´ nl aandeelhouders es Ambiente sv Mellanostern¨ sv offentliga de Unternehmens en biodiversity de arabischen sv privat sv banker cs prˇ´ırody sv Syrien sv offentligt de Aktionare¨ sv biologisk sv Burma fi yksityinen cs banka de ] de Simbabwe cs soukrome´ it azionisti sv naturen de Nahen de offentlicher¨ de Aktien en beauty de Darfur fi yksityisen es preferentes 145

Table A.4: Families, Public Health, Budget, and Energy keywords

Families Public Health Budget Energy it adozioni nl alcohol de Geld de Energiepolitik it famiglia nl roken en money de Strom es hijo en smoking de Gelder de Energie es familia sv alkohol de Mittel de Energien en family sv roka¨ it fondi cs schuze˚ es hijos en alcohol it soldi it gas it figli es tabaco sv pengar es energetico´ en mother en substances it risorse es electrico´ it figlio en tobacco sv medel en energy de Kind es drogas nl geld es energetica´ de Mutter cs kourenˇ ´ı sv pengarna es energ´ıa it coppie sv alkoholpolitik cs prostredkyˇ nl energie it genitori es consumo es dinero de Atomenergie en father nl Motie cs pen´ıze nl elektriciteit sv aktenskap¨ sv alkoholen es fondos sv elmarknaden es padres sv alkoholpolitiken nl middelen sv energi es padre sv tobak en resources de Energieversorgung it bambino sv Systembolaget de ausgegeben de Kraft-Warme-Kopplung¨ nl kind sv narkotika fi rahat fi energiaa en wife fi alkoholia nl besteding de Energiepreise 146

Table A.5: Global (Aid/Climate), Macroeconomy, Media, and Courts / Constitutional keywords

Global (Aid/Climate) Macroeconomy Media Courts/Constitutional de Entwicklungslandern¨ de Konjunktur it RAI sv domstol nl ontwikkelingslanden de Wachstum it frequenze it TAR sv utvecklingslanderna¨ it debito sv TV de Gericht en 0.7 es deficit´ sv tv de Gerichte de Entwicklungslander¨ en deficit de ] sv domstolen de Rio es crecimiento de Film en court nl ontwikkelingssamenwerking sv finanser en television de Richter de Nationen es econom´ıa es television´ it ricorsi es Mundial es inflacion´ en broadcast en courts fi kehitysyhteistyon¨ sv rantan¨ es audiovisual it contenzioso en climate de Finanzpolitik de Rundfunk nl rechter sv globalt it deficit de Medien en judicial nl Ontwikkelingssamenwerking en fiscal es televisiones de Justiz es mundial sv ekonomin nl tv de Klage de WTO de Stabilitats-¨ nl televisie nl rechtbank sv globala sv finanserna de offentlich-rechtlichen¨ es tribunales de Entwicklungspolitik es Monetario en media it tribunali de Entwicklungshilfe de Neuverschuldung nl omroepen en tribunal de BMZ de konjunkturellen it giornalisti de Verfahren sv klimatfragan˚ es Estabilidad en BBC sv instans 147

Table A.6: Jobs, Rights, Prison, and Maritime keywords

Jobs Rights Prison Maritime en unemployed it MoVimento sv kriminalvarden˚ cs schuze˚ en jobseeker de Freiheit es penitenciaria it po en incapacity en liberties it detenuti sv fisket en unemployment en freedom en prison de Schiffe de ] it liberta` it carcere es barcos de arbeitslos sv frihet es penitenciario sv fiskare de Sozialhilfe es libertad nl gevangenis it mare de Arbeitsmarktpolitik nl vrijheid en prisons es buques de Arbeitslosenhilfe en liberty sv anstalter es mar de Arbeitslosigkeit en freedoms it carceri en sea sv arbetsmarknadspolitiken cs PCRˇ it penitenziario en fishermen sv Arbetsformedlingen¨ sv agander¨ atten¨ es penitenciarios it navi sv arbetsformedlingen¨ sv friheten en prisoners nl schip de Arbeitsmarkt de Grundrecht it detenuto cs Poslanecka´ de Arbeitslosen en rights nl gevangenissen it pesca en Work nl vrije nl gedetineerden sv havet de Langzeitarbeitslosen sv integritet nl inrichtingen de Kuste¨ de Arbeitslosengeld de Grundrechte it penitenziaria es pesca en claimants nl rechten en custody en fishing en Plus sv ratten¨ it carcerario de Werften 148

Table A.7: Local / Regional, Religion, Bureaucracy, and Transport keywords

Local/Regional Religion Bureaucracy Transport de Regionen es comunidades nl bureaucratie de Verkehrspolitik de Wahlkreis en Muslim sv Overl¨ aggningen¨ de Verkehrsinfrastruktur nl regio de Muslime en bureaucracy de Bahn es comunidades de Toleranz de Burokratie¨ en Transport sv Overl¨ aggningen¨ en religious it semplificazione en railways sv orter de Islam sv administrativa fi liikennetta¨ nl Groningen de Ehe sv administration nl spoor nl Limburg en gay it amministrazione de Maut it Lombardia en religion it costi de Verkehrssicherheit es Murcia sv religion en tape de Verkehrsminister es Madrid nl religieuze sv byrakrati˚ de Straßenverkehr sv ort en Christian nl vereenvoudiging sv trafikutskottet cs regionu sv diskriminering nl regeldruk es carreteras sv regioner it religione de Burokratieabbau¨ it trasporti sv hemlan¨ cs damy´ en administrative de Straßen sv region cs Damy´ en bureaucratic es Fomento it regioni de Diskriminierung en savings fi liikenne de Bayern en ethnic nl administratieve nl vervoer sv hemkommun es religion´ it burocrazia de Verkehr es ciudades de Religion it sprechi it ferrovie 149

Table A.8: Business, Education, Housing, and Crime keywords

Business Education Housing Crime es pequenas˜ de Schulen de Wohnungsbau cs nazoru´ it imprese de Schule de Wohnungen de Kriminalitat¨ en businesses de Lehrer fi asunto en offence it impresa fi koulun de Wohnung es delitos sv smaf˚ oretagare¨ nl scholen it edilizia de Straftaten de andererseits de Schuler¨ de Wohnungspolitik es Penal en sized it scuole en housing de Verbrechen sv smaf˚ oretagen¨ es educativo it edifici it reato sv smaf˚ oretag¨ fi koulussa de Mieter nl strafmaat en tape en school it immobili es delito sv foretagarna¨ es educativa de Wohneigentum sv brottslighet en small fi koulujen it alloggi it reati it imprenditori en teachers de Wohnraum de Steuerhinterziehung de mittelstandischen¨ en schools sv hus de Polizei de kleinen it insegnanti it abitativa de Tater¨ fi yrityksia¨ it scuola nl woningmarkt en crime en enterprises sv skolpolitik de Wohnen de Strafe es pymes fi kouluissa es vivienda es delincuencia sv foretagare¨ sv huvud sv bostadsutskottet en offences de Mittelstand en teacher es viviendas de Korruption 150

Table A.9: Science / Research, Sport, Taxes, and Workers keywords

Science / Research Sport Taxes Workers es I+D nl nr de Besteuerung en workers cs investice de Sport de Steuerrecht fi tyontekij¨ oiden¨ de Forschung sv idrottsrorelsen¨ de Steuern de Arbeitnehmer es I+D+i sv idrott sv skatteutskottet de Kundigungsschutz¨ de Biotechnologie sv Riksidrottsforbundet¨ en taxation fi tyontekij¨ at¨ de Technologie en sport sv beskattning nl arbeidsvoorwaarden sv Volvo es futbol´ en tax de Arbeitgeber de Innovationen sv idrotten it fiscale it lavoratori de Forschungsforderung¨ de Sports sv skatteutskottets en employees de Technologien en Olympics de Steuerhinterziehung sv fackliga it ricerca en sports nl Belastingdienst es trabajadores de Gentechnik it sport nl belasting es Trabajadores sv forskning sv fotboll sv skatt de Arbeitnehmern es tecnologico´ es deporte es impuestos en employer es investigacion´ sv OS de Steuerpolitik de Gewerkschaften es innovacion´ it sportiva es impuesto de Arbeitsbedingungen nl innovatie it calcio de Steuersystem it dipendenti de Grundlagenforschung de Olympischen nl belastingdienst sv kollektivavtal de Forschungs- it sportive nl fiscale de Beschaftigten¨ de Innovation sv evenemang nl Belastingplan es laborales 151

Table A.10: Defense, Disabilities, OECD / Trade, and Agencies / Bodies keywords

Defense Disabilities OECD / Trade Agencies / Bodies es Armadas en disabilities es Alemania es organismo de Bundeswehr en disabled es Italia sv myndighet en Army en disability de Großbritannien en body es Defensa sv funktionshindrade es Francia es organo´ fi Puolustusvoimien de Behinderungen de Schweden it organismo de Verteidigungshaushalt es discapacidad it Germania it poteri en Defence nl gehandicapten de Frankreich en quangos nl Defensie sv funktionsnedsattning¨ cs Ceskˇ a´ cs pravomoci es ejercito´ sv funktionshinder de Danemark¨ it compiti it militari nl handicap it Francia es independiente de Verteidigungsminister de “ en Germany es funciones cs obrany de Behinderung sv Frankrike en functions es militares en Disability en France es agencia de Streitkrafte¨ nl ondersteund sv Storbritannien sv tillsynen it militare sv Overl¨ aggningen¨ de Italien nl orgaan nl krijgsmacht sv handikappade es Unido en quango sv forsvaret¨ fi Arvoisa en Sweden de Aufgaben it soldati nl gehandicapte it Spagna it organo sv soldater de Behinderte sv Tyskland en accountable de Eurofighter de Behinderten en Canada sv tillsyn 152

Table A.11: Immigration, EU, History / Heritage, and Universities keywords

Immigration EU History / Heritage Universities es inmigrantes es Europea en museum es universidades en immigration es Monetaria sv historia en university es extranjeros it europea en museums en universities de Zuwanderung es Europeo de Denkmal sv studenter es inmigracion´ de Brussel¨ de Geschichte fi opiskelijat en asylum en European sv museer nl universiteiten en migrants cs Evropska´ sv Kulturradet˚ de Hochschulen de Asylbewerber de Europaischen¨ de Gedenkstatten¨ en student en Immigration es europea de Aufarbeitung en students en migration en Europe sv kulturarv it universita` sv flyktingpolitik es Europa en anniversary nl HBO it soggiorno de Europa nl erfgoed de Studierenden es asilo es Estados sv kulturomradet˚ nl studenten de Asylrecht de EU sv museet de Universitaten¨ es extranjer´ıa cs Bruselu sv museum nl universiteit sv invandring sv EU en memorial de Studenten de Aufenthaltsrecht es europeo en heritage de Studium en immigrants es Sovietica´ en Her nl studiefinanciering de Asyl sv unionen sv Riksantikvarieambetet¨ sv studenterna sv arbetskraftsinvandring de Prasidentschaft¨ en history de BAfoG¨ 153

A.2 Rhetorical keywords 154

Table A.12: #Failure, #Studies, #Decisions, and #Statistics keywords

Failure Studies Decisions Statistics de fehlt de Studie es decisiones de Zahl de Ankundigungen¨ sv studie en decisions sv antalet de Statt de Gutachten sv beslut de gestiegen en promises sv undersokning¨ es decision´ sv okning¨ de Fehlanzeige sv utvardering¨ nl beslissingen en trend de versprochen en analysis en decision sv statistik sv vackra es estudio sv besluten sv minskat de Versprechen en study nl beslissing de Ruckgang¨ en failed fi tutkimus de Entscheidungen nl toename es promesas sv rapport de Entscheidung sv statistiken en promised de Untersuchung it conto de zuruckgegangen¨ en promise en survey sv beslutet fi maär¨ a¨ it promesse nl onderzoek es consideracion´ sv okat¨ sv handling de Ergebnisse it decisioni en numbers de Versprechungen sv undersokningar¨ nl besluit de Zahlen en Speech en evaluation nl keuzes sv okar¨ de Taten es analisis´ nl afweging nl daling nl belofte sv analys nl besluiten nl toegenomen es falta sv utvarderingar¨ en steps it numero es intenciones de Untersuchungen en account es aumentado 155

Table A.13: #Compliance, #Nonsense, #Groups, and #Issues keywords

Compliance Nonsense Groups Issues sv avslutad de Behauptung en Association es alturas de z de Vorwurf de ) it Commissioni de Kolleginnen en nonsense it associazioni it linee es sanciones de behauptet es asociaciones it cose nl sanctie de behaupten cs sdruzenˇ ´ı it spalle es inspeccion´ en misleading es organizaciones it missioni it sanzione sv past˚ aenden˚ nl Vereniging sv harmed¨ en ﬁnes de Behauptungen en Society it previsioni it controlli it ’ en Federation it premesse en inspection de falsch de Verbande¨ it considerazioni es sancionador nl onjuist sv organisationer it mozioni nl boete en somehow en organisations it proposte it sanzioni sv past˚ ar˚ es sindicales it questioni en penalties nl misverstand es Asociacion´ it amministrazioni nl sancties sv past˚ aende˚ sv remissinstanser it opposizioni en CQC sv felaktigt de Anhorung¨ it esigenze nl inspectie en neither nl organisaties it Camere en sanctions de Vorwurfe¨ en Institute it generazioni nl boetes de unterstellen en CBI it vicende it veriﬁca en utterly nl vereniging es proposiciones 156

Table A.14: #I Am, #Objectives, #Standards, and #Initiatives keywords

I Am Objectives Standards Initiatives de uberzeugt¨ it obiettivi sv niva˚ nl initiatieven en glad de Ziele de Niveau sv atg˚ arder¨ nl benieuwd sv malet˚ sv nivan˚ cs snemovnouˇ en afraid sv mal˚ nl tarieven nl slot de froh en objectives de Lohne¨ de Projekte en sorry nl doelstellingen en level de Initiativen en grateful it obiettivo nl niveau nl voorlichting de gespannt es objetivos de ” it iniziative en pleased nl doelen nl anderzijds es campanas˜ en sure es objetivo sv priserna nl stimuleren nl blij de Ziel fi taso it promozione de dankbar en objective de Preise de Kampagne en delighted sv malen˚ en threshold cs prevence nl overtuigd it l’obiettivo en high en pilot nl erkentelijk it raggiungere en levels fi piirtein en conscious nl doelstelling nl tarief es colaboracion´ fi tyytyvainen¨ fi tavoite it tassi de Initiative fi iloinen en achieve sv nivaer˚ nl communicatie es convencido sv uppna˚ sv ambitionsniva˚ nl cetera fi huolissani cs c´ıle fi hinta en awareness 157

Table A.15: #Reasons, #Timetable, #Disaster, and #Quotes keywords

Reasons Timetable Disaster Quotes es razones cs PCRˇ es accidente de Zeitung cs duvod˚ nl zomer nl ramp de Artikel nl redenen en timetable it tragedia sv artikel en reasons nl afgerond sv olycka sv lasa¨ nl reden it ’ es danos˜ sv Dagbladet de Grunde¨ es vigor en disaster de lesen en reason sv vanta¨ sv skador sv laste¨ cs duvody˚ sv remiss it cordoglio en read cs procˇ en delay sv olyckor sv artikeln de warum nl wachten sv katastrofer sv s. en explain de Sommerpause de Schaden¨ en Times sv skal¨ nl gereed en tragic en Daily nl argumenten de warten sv stormen de zitiere ﬁ syy en wait en tragedy sv citera de Grund nl klaar en incident de zitieren de Argument en summer it vittime sv tidningen it motivi de vorlegen sv katastrof sv DN sv varfor¨ en recess es catastrofe´ es leer sv Varfor¨ en Assent sv drabbade sv Nyheter sv motiv sv fardig¨ nl brand sv laser¨ 158

Table A.16: #Problems/Solutions, #Questions/Answers, #Comparisons, and #Skepticism keywords

Problems / Solutions Questions / Answers Comparisons Skepticism de Probleme de Antwort it Democratico sv lite de losen¨ de Anfrage en Democrats de scheint es problemas en answer en Democrat de mag de Problem de Antworten de Erstens de bisschen it problemi en questions de erstens es Unidas es problema es preguntas it Comunista nl enerzijds sv problemen nl antwoord it Socialista en might sv problemet it risposta de Drittens cs damy´ sv problem es pregunta sv Ostros¨ en somewhat nl problemen it domanda it Cristiana es Sin en problems en answers it Italiano en bit de gelost¨ de Fragen de Funftens¨ en seem en solve cs odpoveˇdˇ nl Dieren it potrebbe nl probleem en question it comunista en seems en problem cs otazky´ nl Arbeid nl lijkt es resolver it risposte nl ( de konnte¨ sv losa¨ es respuesta sv Bodstrom¨ de Eindruck it problema cs dotaz it Radicale de durchaus de Problems nl antwoorden en Liberals it pare de Losung¨ it domande de Sechstens nl me 159

Table A.17: #Praise, #Differences, #Alternatives, and #Costs keywords

Praise Differences Alternatives Costs de danken nl onderscheid en options nl ondersteund sv tack nl verschil en option de Kosten nl waardering en difference nl optie en costs de Dank sv skillnaden de Alternative en cost it ringraziamento en distinction es alternativa fi kustannukset en tribute de Unterschied fi vaihtoehto nl kosten it ringraziare de zwischen en alternative sv kostnader de bedanken en between de Vorschlag it costi es agradecimiento nl verschillen nl ) sv kostnaderna en pleasure nl kloof nl opties de Lasten es agradecer en differences it Stelle es coste nl complimenten sv mellan de Alternativen it costo en thanks sv skillnader de Losung¨ es Judicial nl maidenspeech fi ero it soluzione sv skattebetalarna en gratitude it l’altro sv huvud en taxpayer nl compliment nl verhouding nl alternatief sv kostnaden cs podekovatˇ es diferencias sv alternativ sv kostnad de Respekt en relationship nl alternatieven de Danke nl danken cs mezi sv losningar¨ sv bara¨ es satisfaccion´ de Unterschiede sv losning¨ de Steuerzahler 160

Table A.18: #Uncertainty, #Change, #My, and #Rules keywords

Uncertainty Change My Rules sv osakerhet¨ sv for¨ andringar¨ de Damen en rules sv oro es cambios en Friend nl regels sv risk en change cs Va´zenˇ e´ cs pravidla en uncertainty sv for¨ andring¨ cs va´zenˇ e´ it regole es incertidumbre en changes de lieben sv regler es inseguridad de Veranderungen¨ en Friends en guidelines en concern nl veranderingen cs mile´ nl richtlijnen it preoccupazioni es cambio en constituency de Regeln it preoccupazione sv for¨ andringarna¨ en constituent es reglas nl onzekerheid sv for¨ andras¨ en constituents sv lagar en fears nl wijzigingen sv anforande¨ nl norm sv riskerar ﬁ muutoksia cs clenovˇ e´ de Kriterien es preocupacion´ cs zmenyˇ sv hemlan¨ en guidance sv risken nl verandering sv kollega en law en risk cs zmenaˇ cs kolegove´ sv reglerna en danger de Reformen sv interpellation de Richtlinien de Angste¨ en changing sv forhoppning¨ nl criteria it rischio en changed sv inledningsanforande¨ nl regelgeving sv radsla¨ sv for¨ andringen¨ de Redezeit nl normen nl Motie sv for¨ andra¨ de Kollegen sv regelverk 161

Table A.19: #Procurement and #Transparency keywords

Procurement Transparency en procurement en information nl enerzijds en transparency cs cena de Transparenz cs zakazky´ it informazioni sv forsvarsindustrin¨ fi tiedot sv krigsmateriel fi tietoja en contract nl openbaarheid sv Saudiarabien de Daten sv materiel cs informace en value nl openheid cs cenu it trasparenza de Kriterien es transparencia sv forsvarsindustri¨ de Informationen nl aanbestedingen sv information cs souteˇzˇ nl transparantie sv export nl informatie sv forsvarsmateriel¨ fi tietoa es Cuentas es informacion´ cs zakazku´ sv insyn cs vyb´ erovˇ e´ nl gegevens 162

Appendix B

Chapter 3

B.1 Attention to subtopics over time

The following ﬁgures show relative attention over time for substantive subtopics not discussed in the main body of the text.

B.1.1 Immigration 163

IDP Scores, United Kingdom IDP Scores, Sweden 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

IDP Scores, Netherlands IDP Scores, Italy 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

IDP Scores, Finland IDP Scores, Spain 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

IDP Scores, Germany IDP Scores, Czech Republic 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Figure B.1: Attention to Internally Displaced Persons over time 164

Applications Scores, United Kingdom Applications Scores, Sweden 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Applications Scores, Netherlands Applications Scores, Italy 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Applications Scores, Finland Applications Scores, Spain 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Applications Scores, Germany Applications Scores, Czech Republic 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Figure B.2: Attention to Applications over time 165

Terrorism Scores, United Kingdom Terrorism Scores, Sweden 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Terrorism Scores, Netherlands Terrorism Scores, Italy 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Terrorism Scores, Finland Terrorism Scores, Spain 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Terrorism Scores, Germany Terrorism Scores, Czech Republic 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Figure B.3: Attention to Terrorism over time 166

EU Scores, United Kingdom EU Scores, Sweden 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

EU Scores, Netherlands EU Scores, Italy 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

EU Scores, Finland EU Scores, Spain 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

EU Scores, Germany EU Scores, Czech Republic 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Figure B.4: Attention to EU over time 167

B.1.2 Religion 168

Charity Scores, United Kingdom Charity Scores, Sweden 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Charity Scores, Netherlands Charity Scores, Italy 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Charity Scores, Finland Charity Scores, Spain 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Charity Scores, Germany Charity Scores, Czech Republic 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Figure B.5: Attention to Charity over time 169

Families Scores, United Kingdom Families Scores, Sweden 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Families Scores, Netherlands Families Scores, Italy 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Families Scores, Finland Families Scores, Spain 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Families Scores, Germany Families Scores, Czech Republic 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Figure B.6: Attention to Charity over time 170

Violence Scores, United Kingdom Violence Scores, Sweden 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Violence Scores, Netherlands Violence Scores, Italy 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Violence Scores, Finland Violence Scores, Spain 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Violence Scores, Germany Violence Scores, Czech Republic 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Figure B.7: Attention to Violence over time 171

Gender Scores, United Kingdom Gender Scores, Sweden 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Gender Scores, Netherlands Gender Scores, Italy 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Gender Scores, Finland Gender Scores, Spain 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Gender Scores, Germany Gender Scores, Czech Republic 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Figure B.8: Attention to Gender over time 172

Schools Scores, United Kingdom Schools Scores, Sweden 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Schools Scores, Netherlands Schools Scores, Italy 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Schools Scores, Finland Schools Scores, Spain 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Schools Scores, Germany Schools Scores, Czech Republic 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Figure B.9: Attention to Schools over time 173

B.1.3 Crime 174

Courts Scores, United Kingdom Courts Scores, Sweden 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Courts Scores, Netherlands Courts Scores, Italy 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Courts Scores, Finland Courts Scores, Spain 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Courts Scores, Germany Courts Scores, Czech Republic 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Figure B.10: Attention to Courts over time 175

Rights Scores, United Kingdom Rights Scores, Sweden 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Rights Scores, Netherlands Rights Scores, Italy 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Rights Scores, Finland Rights Scores, Spain 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Rights Scores, Germany Rights Scores, Czech Republic 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Figure B.11: Attention to Rights over time 176

Police Scores, United Kingdom Police Scores, Sweden 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Police Scores, Netherlands Police Scores, Italy 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Police Scores, Finland Police Scores, Spain 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Police Scores, Germany Police Scores, Czech Republic 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Figure B.12: Attention to Police over time 177

year year

Figure B.13: Attention to Prison over time 178

Political Violence Scores, United Kingdom Political Violence Scores, Sweden 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Political Violence Scores, Netherlands Political Violence Scores, Italy 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Political Violence Scores, Finland Political Violence Scores, Spain 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Political Violence Scores, Germany Political Violence Scores, Czech Republic 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Figure B.14: Attention to Political Violence over time 179

B.1.4 Public Health 180

Alcohol Scores, United Kingdom Alcohol Scores, Sweden 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Alcohol Scores, Netherlands Alcohol Scores, Italy 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Alcohol Scores, Finland Alcohol Scores, Spain 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Alcohol Scores, Germany Alcohol Scores, Czech Republic 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Figure B.15: Attention to Alcohol over time 181

Animals Scores, United Kingdom Animals Scores, Sweden 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Animals Scores, Netherlands Animals Scores, Italy 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Animals Scores, Finland Animals Scores, Spain 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Animals Scores, Germany Animals Scores, Czech Republic 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Figure B.16: Attention to Animals over time 182

Children Scores, United Kingdom Children Scores, Sweden 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Children Scores, Netherlands Children Scores, Italy 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Children Scores, Finland Children Scores, Spain 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Children Scores, Germany Children Scores, Czech Republic 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Figure B.17: Attention to Children over time 183

Consumers Scores, United Kingdom Consumers Scores, Sweden 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Consumers Scores, Netherlands Consumers Scores, Italy 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Consumers Scores, Finland Consumers Scores, Spain 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Consumers Scores, Germany Consumers Scores, Czech Republic 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Figure B.18: Attention to Consumers over time 184

B.1.5 Europe 185

Markets Scores, United Kingdom Markets Scores, Sweden 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Markets Scores, Netherlands Markets Scores, Italy 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Markets Scores, Finland Markets Scores, Spain 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Markets Scores, Germany Markets Scores, Czech Republic 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Figure B.19: Attention to Markets over time 186

Eurozone Scores, United Kingdom Eurozone Scores, Sweden 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Eurozone Scores, Netherlands Eurozone Scores, Italy 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Eurozone Scores, Finland Eurozone Scores, Spain 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Eurozone Scores, Germany Eurozone Scores, Czech Republic 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Figure B.20: Attention to Eurozone over time 187

NATO Scores, United Kingdom NATO Scores, Sweden 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

NATO Scores, Netherlands NATO Scores, Italy 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

NATO Scores, Finland NATO Scores, Spain 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

NATO Scores, Germany NATO Scores, Czech Republic 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Figure B.21: Attention to NATO over time 188

Threats Scores, United Kingdom Threats Scores, Sweden 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Threats Scores, Netherlands Threats Scores, Italy 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Threats Scores, Finland Threats Scores, Spain 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Threats Scores, Germany Threats Scores, Czech Republic 0.6 0.6 0.2 0.2 −0.2 −0.2 Party−Year Scores Party−Year Scores Party−Year −0.6 −0.6 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015

year year

Figure B.22: Attention to Threats over time 189

Appendix C

Chapter 4

C.1 Government / Opposition, all parties

Figure C.4 shows the difference in relative attention when nationalist parties are present, disaggregated by whether or not parties are in government or in opposition. Few conclusions can be drawn from this. It is unlikely that we would see general effects of an opposition vs. government reaction to the radical–right, as any response would be mediated by ideology and the threat of those radical–right parties to the establishment’s electoral position.

C.2 Liberals and Christian Democrats

Liberals and Christian Democrats differ from Conservatives and Social–Democrats, as they are often mainstream parties, but far more heterogeneous, both in their ideological positions as well as their time spent in government. As such, it is difﬁcult to draw clear theoretical conclusions, as it is for Conservatives and Social Democrats. This section presents the various differences in relative attention to topics, with and without nationalist parties seated in the legislature. We can further disaggregate the effects to compare Christian Democrats and Liberals when they are in and out of government. 190

Figure C.1: Relative attention to topics for government and opposition, by radical right seated

NAT parties present vs. no NAT seats, GOV parties

Global (Aid/Climate) ● Education ● Families ● Universities ● #Timetable ● #Costs ● Rights ● Private/Public ● Disabilities ● #Deliberations ● Pensions ● #Procurement ● Crime ● Representation ● Telecom ● Media ● Agencies/Bodies ● #Failure ● Sport ● Agriculture ●

−0.04 −0.02 0.00 0.02 0.04

Less attention ~ More attention

NAT parties present vs. no NAT seats, OPP parties

Health ● Transport ● Crime ● Maritime ● Telecom ● Banks ● #Praise ● #Disaster ● Environment ● Jobs ● OECD/Trade ● #Groups ● Budget ● Representation ● Religion ● Rights ● Discrimination ● Macroeconomy ● #Nonsense ● #Skepticism ●

−0.03 −0.02 −0.01 0.00 0.01 0.02 0.03

Less attention ~ More attention 191

Figure C.2: Relative attention to topics for Liberals and Christian Democrats, by radical right seated

Christian Democratic

Education ● Banks ● Health ● #Nonsense ● Environment ● Global (Aid/Climate) ● #Comparisons ● #Failure ● Universities ● Maritime ● #Issues ● Science/R&D ● #Deliberations ● Rights ● Local/Regional ● Macroeconomy ● Agencies/Bodies ● Crime ● Discrimination ● International Crises ●

−0.10 −0.05 0.00 0.05 0.10

Less attention ~ More attention

Liberal

#Standards ● Health ● Inequality ● Workers ● #Nonsense ● #Timetable ● #Deliberations ● Transport ● Immigration ● #Questions & Answers ● Crime ● #Studies ● #Objectives ● Representation ● #Initiatives ● Universities ● Sport ● Environment ● #Praise ● Agriculture ●

−0.10 −0.05 0.00 0.05 0.10

Less attention ~ More attention 192

Figure C.3: Relative attention to topics for Christian Democrats, by radical right seated, disaggregated by opposition status

Christian Democrats, in opposition

Education ● Banks ● Health ● #Failure ● Environment ● Private/Public ● Universities ● #I_am_ ● #Quotes ● Global (Aid/Climate) ● Discrimination ● #Decisions ● #Praise ● #Timetable ● OECD/Trade ● International Crises ● Local/Regional ● Rights ● Crime ● Macroeconomy ●

−0.10 −0.05 0.00 0.05 0.10

Less attention ~ More attention

Christian Democrats, in government

Families ● #Nonsense ● #Compliance ● #Comparisons ● #Decisions ● Macroeconomy ● Taxes ● Education ● Agreements ● Business ● #Issues ● Pensions ● Courts/Constitutional ● Science/R&D ● Representation ● Workers ● #Quotes ● Discrimination ● International Crises ● Agencies/Bodies ●

−0.10 −0.05 0.00 0.05 0.10

Less attention ~ More attention 193

Figure C.4: Relative attention to topics for Christian Democrats, by radical right seated, disaggregated by opposition status

Liberals, in opposition

Immigration ● Health ● #Compliance ● Crime ● Telecom ● #Timetable ● Europe ● #Standards ● #Nonsense ● #Consequences ● Representation ● Rights ● #Differences ● #Praise ● Discrimination ● #Failure ● Global (Aid/Climate) ● Science/R&D ● Environment ● Agriculture ●

−0.10 −0.05 0.00 0.05 0.10

Less attention ~ More attention

Liberals, in government

Workers ● Transport ● Private/Public ● International Crises ● Rights ● Local/Regional ● Inequality ● #Standards ● #Quotes ● #Questions & Answers ● Universities ● Europe ● Prisons ● #Initiatives ● #Praise ● Telecom ● Sport ● #Rules ● Agencies/Bodies ● Crime ●

−0.10 −0.05 0.00 0.05 0.10

Less attention ~ More attention Mitchell Goist 224 Pond Lab ⋅ University Park, PA 16802 � [email protected] | � www.mitchellgoist.github.io | � mitchellgoist

Education The Pennsylvania State University State College, PA PH.D. POLITICAL SCIENCE; MINOR IN SOCIAL DATA ANALYTICS 2013-2019 (expected)

Hendrix College Conway, AR B.A. IN INTERNATIONAL RELATIONS 2009-2013

Publications 2019. “Reconstructing and Analyzing the Transnational Human Trafficking Network.” In Proceedings of the 2019 IEEE ACM International Conference on Advances in Social Networks Analysis and Mining. WITH CHRISTOPHER BOYLAN AND TED CHEN 2018. “Traditional Institutions and Social Cooperation: Experimental Evidence from the Buganda Kingdom” Research and Politics 5:1 WITH FLORIAN KERN Forthcoming. “Taking Data Seriously in the Design of Data Science Projects” SAGE Handbook of Research Methods in Political Science and International Relations WITH BURT MONROE

Working Papers “Analysis of Politcal Text in Multiple Languages” WITH BURT MONROE “The Radical Right in Parliament: Multi-dimensional scaling of far-right parties” “Concession and Confrontation: Mainstream parties response to radical right success”

“Measuring Hierarchy in Social Networks” WITH MATTHEW J. DENNY

Work Experience

Spring Instructur of Record for PLSC 309: Quantitative Political Analysis, 2019 Summer Data Science for the Social Good at University of Washington, 2017 AY Instructor of record for AFR 110: Introduction to Contemporary Africa, 2015-2016 AY Consultant for USAID GAPP (through Social Impact), 2014-2015 Summer Randomized Control Trial Evaluation for USAID, 2015

APRIL 20, 2020 MITCHELL GOIST · CURRICULUM VITAE 1