Nick Beauchamp

This paper shows how legislators' written and spoken text can be used to ideologically scale individuals even in the absence of informative votes, by positioning members according to their similarity to two reference texts constructed from the aggregated speech of every member of each of two major parties. The paper develops a new Bayesian scaling that is more theoretically sound that the related Wordscores approach, and a new vector-based scaling that works better than either at matching the vote-based scaling DW-Nominate in the US Senate. Unsupervised methods such as Wordfish or principal component analysis are found to do less well. Once validated in the US context, this approach is then tested in a legislature without informative voting, the UK House of Commons. There the scalings successfully separate members of different parties, order parties correctly, match expert and rebellion-based scalings reasonably well, and work across different years and even changes in leadership. The text-based scaling developed here both matches existent scalings fairly well, and may be a much more accurate window into the true ideological positions of political actors in legislatures and the many other domains where textual data are plentiful.

This paper presents a new, bottom-up approach to modeling the effects of television advertising on vote intention. Because ads are so numerous and varied, existing studies generally begin with a specific theory of persuasion, or must simplify the data down to a few latent dimensions or effective ads. Instead, this new approach first develops a one-at-a-time regression technique to estimate the effects of hundreds of different ads on vote intention during the 2004 presidential campaign. The aggregate effect of advertising is found to be significant, though many individual ads have small or backfiring effects. To explain these varying effects, new automated text analysis procedures are developed which can predict the effects of ads based only on their text, and reveal complex and asymmetric strategies that mix affect, policies, issue ownership, negativity, and targeting. This bottom-up procedure constitutes a new method for understanding persuasion in campaigns and politics more broadly.

Political opinion, and hence political behavior, is shaped largely via talk: with family, friends, and increasingly, online. But such discussions are often taken to be unstructured ideological posturing with little persuasive effect. This paper instead proposes a more deliberative model, where argument consists of the strategic exchange of topics, frames and ideas that are interconnected in a complex conceptual network. This network of ideas is inferred using new Bayesian topic modeling methods applied to a new dataset of millions of political discussions from the largest political forum online. By modeling arguments as a Markov process with the network as transition matrix, we can predict what topics arguers will deploy in response to each other: contrary to framing or expressive models of speech which predict that speakers will echo or ignore their interlocutor, this new model shows discussion to be more deliberative, where speakers offer ideas, facts, and topics relevant to, but missing from, what their interlocutor has said. In the longer term, panel vector autoregression methods reveal that a significant subset of users appear to change their views in response to what they hear, although listeners are biased against speech too unlike their own. Finally, because users can vote to recommend posts, factor analysis of this voting data reveals a strong underlying ideological dimension, largely centering (on this mainly Democratic forum) around criticism or praise of Obama. This ideological behavior is illuminated by the conceptual network: we see criticism of Obama largely based on left-wing policy issues, whereas praise is largely emotional and personal. This asymmetry is consistent with numerous psychological models of ideology, and also reveals which discussed topics may influence voting behavior in the long term. This text-based network of ideas allows us to model both the short-term dynamics of political argument and long-term opinion change, using a framework that is as rich, complex, and substantively interpretable as people have always claimed their arguments were.

This paper finds that the decisions of the Supreme Court over the last 10 years can be systematically predicted using the text of the briefs and oral arguments that precede those decisions. This automated text analysis also sheds new light on the variety of mechanisms underlying decisions, including the tradeoffs between political and procedural decision-making. Support vector machines and ensembles of univariate regressions are first used to predict decisions of the court as a whole and of individual judges, with up to 62\% (out-of-sample) accuracy -- better than many experts. These ensemble methods are then used to extract a small subset of words that are significantly associated with tendencies in the court and individual judges to vote more liberally or more conservatively. These terms reveal a large array of different decision-making strategies at work, with implications for the ongoing debates between legal reasoning and precedent on the one hand, and policy preferences and ideological attitudes on the other. The decisions of the conservative justices appear especially predictable, revealing a tendency to vote more conservatively on constitutional and criminal topics, but more liberally when briefs emphasize the intentions of Congress and statutory language. Analysis of oral transcripts reveals how the qualities of debate affect decisions, in particular a tendency for some conservative judges to vote more liberally when the conversation is intense but marked with laughter. Together, the text of briefs and oral arguments provide distinct but complementary insights into the varied decision-making of different justices. More broadly, this approach both constitutes a useful predictive tool, and sheds light on numerous existing theories of judicial decision-making while suggesting many new avenues of exploration.

This paper introduces a new maximization and sampling algorithm, "Blossom," along with an associated R package, which is especially well suited to rugged functions where even approximate gradient methods are unfeasible, such as those encountered in highly nonlinear likelihoods and in matching for causal inference. The Blossom algorithm is an evolutionary strategy related to the Estimation of Multivariate Normal Algorithm (EMNA) or Covariance Matrix Adaptation (CMA), within the general family of Estimation of Distribution Algorithms (EDA). It works by successive iterations of sampling, selecting the highest-scoring subsample, and using the variance-covariance matrix of that subsample to generate a new sample, with various self-adapting parameters. Compared against a benchmark suite of challenging functions introduced in Yao, Liu, and Lin (1999), it finds equal or better maxima to those found by the genetic algorithm Genoud introduced in Mebane and Sekhon (2011). The algorithm is then applied to two real-world problems from political science: maximizing a difficult likelihood function combining both utility and spatial metric parameters, and two high-dimensional matching problems, where it produces better results than many existing packages in R such as GenMatch (Sekhon, 2011). Finally, to utilize the samples generated in the process of maximization to accurately sample from the posterior likelihood, approximate voronoi cells around sample points are used to approximate numerical integrals. This sampling method is shown to produce better results than a simple metropolis MCMC sampler using benchmark distributions such as the "banana" function.

Most spatial models of preference assume that the spaces in question are Euclidean, and that utility functions are quadratic. Although increasing work has recently been done in estimating utility functions from empirical data, and some theoretical work has been done with non-Euclidean spatial metrics, relatively little has been done to estimate spatial metrics from empirical data in the political context. This paper employs maximum likelihood techniques to directly estimate both spatial metrics and utility functions from ANES survey data. A simulation is also conducted to confirm that these methods can indeed accurately recover spatial and utility parameters. The results show that in the most general case, the spatial metric appears close to Euclidean, but the utility function is much less "risk-adverse" than generally assumed. Furthermore, different combinations of issues produce different estimates for both spatial metrics and utility functions, although in all cases the utility functions are far from quadratic. Of practical importance, coefficients on policy variables appear to vary with different spatial metrics and utility functions, indicating that assumptions made about the metric of a space may be biasing empirical results.

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