Spatial Models Of Parliamentary Voting

This paper develops a novel unsupervised algorithm for belief representation learning in polarized networks that (i) uncovers the latent dimensions of the underlying belief space and (ii) jointly embeds users and content items (that they interact with) into that space in a manner that facilitates a number of downstream tasks, such as stance detection, stance prediction, and ideology mapping. Inspired by total correlation in information theory, we propose the Information-Theoretic Variational Graph Auto-Encoder (InfoVGAE) that learns to project both users and content items (e.g., posts that represent user views) into an appropriate disentangled latent space. To better disentangle latent variables in that space, we develop a total correlation regularization module, a Proportional-Integral (PI) control module, and adopt rectified Gaussian distribution to ensure the orthogonality. The latent representation of users and content can then be used to quantify their ideological leaning and detect/predict their stances on issues. We evaluate the performance of the proposed InfoVGAE on three real-world datasets, of which two are collected from Twitter and one from U.S. Congress voting records. The evaluation results show that our model outperforms state-of-the-art unsupervised models by reducing 10.5% user clustering errors and achieving 12.1% higher F1 scores for stance separation of content items. In addition, InfoVGAE produces a comparable result with supervised models. We also discuss its performance on stance prediction and user ranking within ideological groups. 

/pdf/unsupervised-belief-representation-learning-with-information-maoxgtqs.pdf

Unsupervised Belief Representation Learning with Information-Theoretic Variational Graph Auto-Encoders

A supply-side approach to corporate political activity: Performance consequences of ideologically driven CPA

Abstract Ideological divisions in the United States have become increasingly prominent in daily communication. Accordingly, there has been much research on political polarization, including many recent efforts that take a computational perspective. By detecting political biases in a text document, one can attempt to discern and describe its polarity. Intuitively, the named entities (i.e., the nouns and the phrases that act as nouns) and hashtags in text often carry information about political views. For example, people who use the term “pro-choice” are likely to be liberal and people who use the term “pro-life” are likely to be conservative. In this paper, we seek to reveal political polarities in social-media text data and to quantify these polarities by explicitly assigning a polarity score to entities and hashtags. Although this idea is straightforward, it is difficult to perform such inference in a trustworthy quantitative way. Key challenges include the small number of known labels, the continuous spectrum of political views, and the preservation of both a polarity score and a polarity-neutral semantic meaning in an embedding vector of words. To attempt to overcome these challenges, we propose the P olarity-aware E mbedding M ulti-task learning ( PEM ) model. This model consists of (1) a self-supervised context-preservation task, (2) an attention-based tweet-level polarity-inference task, and (3) an adversarial learning task that promotes independence between an embedding’s polarity component and its semantic component. Our experimental results demonstrate that our PEM model can successfully learn polarity-aware embeddings that perform well at tweet-level and account-level classification tasks. We examine a variety of applications—including a study of spatial and temporal distributions of polarities and a comparison between tweets from Twitter and posts from Parler—and we thereby demonstrate the effectiveness of our PEM model. We also discuss important limitations of our work and encourage caution when applying the PEM model to real-world scenarios. 

/pdf/detecting-political-biases-of-named-entities-and-hashtags-on-116uhs5g.pdf

Detecting political biases of named entities and hashtags on Twitter

Income Inequality and Electoral Theories of Polarization

For the last 40 years, Keith Poole has developed and curated a trove of basic data and measurements related to the United States Congress. He has made these resources freely available through his widely-used Voteview.com website since 1995. At Poole’s Voteview.com, scholars, students, journalists, and the broader public could download gold-standard historical and current roll-call voting data, member rosters, NOMINATE scores, and measures and visualizations of party cohesion and polarization, among many other useful things. In this article, we describe how we are preserving and continuing these vast public-goods contributions through the new Voteview.com. Developed and housed at UCLA, the new Voteview.com carries on the creation of basic roll-call data infrastructure, including the assignment of Poole’s widely-used ICPSR number-like identifiers to new members, data on every roll-call vote ever taken, NOMINATE scores and other standard roll-call vote-based measures such as party-loyalty scores. In addition to serving as a platform for the continuation and dissemination of this basic data infrastructure, the new Voteview.com also provides powerful tools for exploring the history of roll-call voting, the US Congress, and American politics and political history through a simple search interface and interactive visualizations.

The new Voteview.com: preserving and continuing Keith Poole’s infrastructure for scholars, students and observers of Congress

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The new Voteview.com: preserving and continuing Keith Poole’s infrastructure for scholars, students and observers of Congress