Need an account? Click here to sign up. Download Free PDF. Lenka Bustikova. A short summary of this paper. Download Download PDF. Translate PDF. Email: lenka. Email: david. Email: salashri kacst. Subject matter experts validate the approach and interpret the results. Keywords: forecasting, automated content analysis, natural language processing, probabilistic topic modeling, sparse learning, political parties, polarization One of the most fundamental questions in party politics is whether, and if so when, parties react to other parties.
Advances in machine learning and the availability of highly granular textual data make progress on this question possible in a manner than was not previously feasible on a large scale. This paper develops a computational system, building upon recent advances in natural language processing, to analyze partisan debate and responsiveness with a broad potential utility for studying the dynamics of political competition across different scales and contexts.
Hotly debated issues span all spheres of human activity, but politics is perhaps the sphere most defined by contentious debates, and much of it is now fully documented online and available for textual analysis. Text mining tools enable researchers to engage in the systematic analysis of text as data in an unprecedented manner, and political scientists have often been at the forefront of developing Political Analysis DOI: Earlier versions of the paper were presented in Amsterdam at the R.
The project c The Author s We especially thank Carolyn Forbes by Cambridge University Press for helping to initiate and sustain the project. Supplementary materials for this article are available on the Political Analysis on behalf of the Society for website. For Dataverse replication materials, see Alashri et al. Political Methodology. With few exceptions Meguid , most of the empirical studies that investigate partisan responsiveness focus on competition between mainstream parties or between the mainstream parties and smaller parties in their ideological families Adams and Somer-Topcu Political science as a discipline still knows relatively little about this fundamental issue and lacks tailored methods to analyze the dynamics of polarization originating from the interaction of political parties at the extreme poles of the political spectrum.
In order to detect centrifugal tendencies in the party system, we focus directly on the most extreme poles of the party spectrum that drive polarization. The more common approach is to focus on interactions between parties that are ideologically related and spatially proximate Katz and Mair ; Arzheimer and Carter ; Abou-Chadi and Krause , cf.
Bustikova , whereas here we study parties that are ideologically and spatially opposite. Scholars have found that niche parties radical right, ethnic, environmental and regionalist are less responsive to the preferences of the general electorate and to other parties than are mainstream parties Adams et al.
Niche parties are important vehicles of political polarization Sartori ; Ignazi ; Evans ; Meguid The dynamic of responsiveness between two rival niche party families, studied in this paper, can enhance our understanding of the dynamics of multiparty systems of polarized pluralism Sartori In such party systems, electoral advantage stems from centrifugal competition. Extending these approaches, Salton proposed an automated document similarity measure to process large data collections in an automated fashion.
Grimmer applied Bayesian Hierarchical Topic Modeling to identify political agendas expressed in the press releases from senators and Monroe et al. More recently, Greene and Cross developed a new dynamic topic modeling method based on two layers of nonnegative matrix factorization and demonstrated that it can unveil new niche topics and associated vocabularies using a corpus of all English language legislative speeches in the European Parliament plenary.
Theocharis et al. Lenka Bustikova et al. If niche parties choose to respond to their polar opponent, they can have a harmful impact on the ability of the party system to rally around the center. By weakening centripetal competition, these dynamics contribute to volatility, fragmentation and de-alignment, and thereby undermine the ability of institutionalized mainstream parties to achieve moderation.
Small, niche parties are often overlooked because they appear marginal at the macro-electoral level and their supporters are missed by surveys. However, an advantage of text mining is that it allows us to capture the dynamics of responsiveness among small parties that often play an outsized role in party system polarization due to their focus on single issues and ideological purity. The approach utilized here captures new, small, ascending parties and factions that have contributed in important ways to public discourse and to political polarization.
Although ideological opposites seemingly compete on the same cultural dimension, they strategically highlight and suppress their reactions to some topics that their opponents raise. In the long run of an electoral cycle, the dynamics of counter-reactions can wash out, giving the false impression that niche parties are less responsive than mainstream parties, but this is at odds with the microdynamics highlighted in this analysis. The volatile nature of identity politics indicates that polarization is often driven by microbursts that can quickly escalate contestation and, subsequently, recede.
Text mining allows us to disaggregate the identity dimension of party competition and, by looking at a multiplicity of topics, to identify with a high level of precision which topics elicit reactions and which are ignored. This paper contributes to a growing literature using text mining to learn party positions from texts e. Building on ideas introduced in Monroe et al. Our main methodological contribution is to introduce a new system for detecting, analyzing and predicting partisan responsiveness between political rivals, which we believe has potentially broad application across a variety of contexts and at different levels of analysis.
We simultaneously detect topics that are ignored by the adversarial camps and, using country-specific knowledge, explain the strategic logic that leads party leadership to escalate selectively. We show that SLEP performs very favorably. Based on the F-measure, LDA offers the best model. For recent applications in political science to conflict, see Muchlinski et al.
If so, we then ask whether proximate spikes from an opposing camp are related, and can we use this information to predict partisan responsiveness? The result is a framework that can model how political discourse varies over time, detect topics that gain disproportionate attention from each camp and predict which topics solicit reactions from political rivals and which topics are ignored. Once topical spikes from political opponents are detected and categorized using LDA Blei et al.
These results compare very favorably to experimentally tuned Naive Bayes and Random Forest classifiers. Figure 1 offers a stylized overview of the system architecture. The numbers on the top left corner of each box represent the order in which these processes are executed. Each of the seven steps in the process is briefly described below, with additional details in the following sections.
System Architecture. In step 1, we wrote a set of scripts to download all documents from the websites of radical right parties and ethnic political parties in Slovakia from the beginning of the calendar year to March 16, Next, we preprocessed the data to extract text and article dates. This measure identifies the importance of a word to a document based on its presence in a document TF and its rarity at the corpus level IDF.
Select the top T n-gram terms 1—3 grams. Next, we describe these steps in greater detail. We first collected all the documents, generated a ranked list of n-gram keywords and placed the most frequent n-grams on the top of the list. Our approach builds on Shah et al. Two experts, Bustikova and Siroky, coded topics.
For validation, the two coders achieved average inter-coder reliability Kripendorff ratio of. The Supplementary material section: Expert Validation lists discriminative keywords. We also discuss how the experts selected topics and how this approach compares with the Manifesto Data Project and with the Chapel Hill Expert Survey in the Supplementary materials.
As a classification system, it places political orientations into four categories using the two axes of grid and group: hierarchy, egalitarianism, individualism and fatalism. It offers a more nuanced analytical tool for party classification than the left— right placement and is more versatile than the commonly used traditional versus libertarian distinction used in the Chapel Hill Expert Survey.
It does not collapse identity onto one dimension and therefore can account for the fact that ethnic inclusion does not necessarily imply social liberalism. To capture different aspects of polarization and more granular action—reaction dynamics, the identity axis needs to be disaggregated.
Grid—group allows the analyst to classify attitudes toward state authority as separate from ethnic issues. We utilize LDA, one of the most popular topic inference algorithms Blei et al.
It assumes that documents represent a mixture of topics, where a topic is a probability distribution over words. For each grid—group issue, we determine when an issue is salient for one party i. We tuned this sliding window to 20 weeks because it showed the best performance. When smaller windows 5 weeks, 10 weeks and 15 weeks are applied, the resultant spikes are noisy.
When larger windows are applied 25 weeks, 30 weeks and 35 weeks , the resultant spikes are sparse. This implies that, by definition, nationalist parties cannot embrace gender equality and minority ethnic parties are socially liberal. The first time, LDA is applied on the overall corpus both radical and ethnic corpus to measure the relatedness of spikes. The second time, LDA is used separately on the radical corpus radical right parties and on the ethnic corpus ethnic parties to determine issue- specific frames to be exploited as features for the predictive model.
It is a highly effective method of capturing semantic relations where each document is represented by a real number vector such that similar documents are closer to one another than dissimilar documents in a geometric space. Then, we determine the labels of spikes based on the KL measure, which captures the divergence of distributions between two consecutive spikes Equation 3.
The following steps describe our algorithm: 1 For each key grid—group issue, run LDA to get latent topics for one camp. P The sparse-learning approach SLEP relies on a gradient descent algorithm to solve the above convex and nonsmooth optimization problem Liu et al.
The frames with nonzero values 13 Cosine fits here better as each document is represented by a point in a geometry space, and through thresholding approach, we determined labels of spikes.
Among these approaches, LDA tends to be most resilient when the number of topics, k , increases Blei et al. However, larger k imposes additional computational costs and makes convergence of the posterior probability estimate more difficult.
Finding the right k also requires qualitative validation by experts. Our techniques have been used to launch and sustain the careers of artists around the world. Whether you're a serious vocalist interested in taking your professional skills to the next level or a beginner who just wants to sing for fun, you've come to the right place.
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