I also have a broad interest in Bayesian statistics and Monte Carlo simulation, inferring networks from overlapping language, and in tracing the diffusion of ideas in political media. For more on these, see my projects below.
Chapter 1 builds on my past work and contributes to the structural topic modeling approach made popular by Roberts, Stewart, Tingley & Airoldi (2013). With a theoretically-motivated model of ideological rhetoric, I show the advantages of adding additional structure to the traditional logistic-normal topic model, especially for measuring complex language features like ideological perspective. This additional structure comes from three sources: variation in ideological language over time; differences in ideological attention to various topics, and differences in the tone with which ideologues discuss political issues. The first two components are employed widely in political methodology, though almost always separately; and tone---i.e., sentiment, or positivity/negativity of language---is not often discussed in political research. This chapter resolves that gap by providing a straightforward model and inference procedure to account for all three sources of information simultaneously.
Chapter 2 explores the use of deep learning techniques for the classification of political language. ANNs are models that attempt to learn patterns in data by passing information through layers of nodes, each of which applies a regression-like weighting function to the input. The structure of multi-layer ANNs allow them to account for high-order interaction and nonlinearities when mapping inputs (e.g., word or phrase use), to outputs (e.g., document labels, speaker ideology, sentiment). This, in turn, can improve classification accuracy in some natural language processing contexts. Deep-learning neural networks have not been discussed broadly in political science methodology, and this article shows how ANNs can serve as an important classification tool in computational text modeling.
The chapter has three methodological sections: 1. constructing multilayer neural networks and estimating model parameters; 2. making inferences on new data; and 3. assessing performance and avoiding common problems like overfitting. I then describe additional possible structures for ANNs, paying particular attention to how word embedding vectors can capture even complex lexical features in text. Word embeddings, made popular by Google's word2vec or Stanford NLP's GloVe, reduce the dimensionality of text data by projecting words into a lower-dimensional space. Distances between words in this space serve as a measure of how similar words or phrases are to each other. In both Monte Carlo and real-data applications, I show that ANNs, achieve competitive classification accuracy compared to other methods. This performance is often improved by including word embedding vectors in the ANN specification.
The third chapter of my dissertation explores the dynamics of political rhetoric. Previous work on elite responsiveness to public opinion suggests that leaders should tailor their political messages to prevailing public opinion. Using the methods described in the first two chapters, I measure the ideological content of political rhetoric over time. First, I explore the ideological content of campaign speeches by major candidates in the 2008, 2012 and 2016 elections, and measure the relationship between their language and the dynamics of the campaign. Second, I measure the variation in presidential rhetoric using major speeches and statements over the course of Obama's presidential term, and compare the ideological content of his statements against aggregate public opinion.
I show that over the course of presidential elections, candidates tend to adapt to prevailing opinion of the electorate. During the primaries, candidates tend to maintain fairly partisan language, but they move strategically even within this electoral stage. Barack Obama began his primary campaign using predominantly liberal language. As Obama's popularity and delegate count grew, he transitioned slowly to a more generalist, balanced rhetoric we would expect of a major-party nominee. Meanwhile, Clinton followed an opposite trajectory. At the start of the primary, when Clinton was the prohibitive favorite for the nomination, she employed fairly moderate ideological language. As she lost ground to Obama, however, she began adopting an increasingly liberal voice.
"Emotional Responses to Disturbing Political News: The Role of Personality" with Timothy J. Ryan and Matthew S. Wells. Journal of Experimental Political Science. Forthcoming. abstract
"Measuring Ideological Proportions in Political Speeches" with Yanchuan Sim, Justin H. Gross & Noah A. Smith. In Empirical Methods in Natural Language Processing. 2013. abstract | pdf | supplemental materials | replication
"IdEaS: The Ideological Etch-a-Sketch model: Inferring Ideology from Key Phrases in Text" with Justin H. Gross. Political Methodology Series. Dept. of Politics, Princeton University. February 2012.
My primary teaching interests center on methodological training for graduate and undergraduate students; particularly: probability theory, linear and generalized linear models, Bayesian statistics, latent variable and scaling models, text-as-data models and introductory machine learning. In American politics, my teaching interests include ideology, political behavior, campaign and elections, and political communication.
At Carolina, I have served as TA for multiple graduate-level methods courses. As TA, I've taught a weekly one-credit-hour lab in addition to holding office hours, contributing questions to assignments and grading problem sets. Labs cover computational statistics in R, as well as supplemental material, background mathematics and typesetting in LaTeX.
In Summer 2015, I served as a Teaching Assistant for two courses at the Inter-University Consortium for Social and Political Research (ICPSR) summer program: advanced Bayesian methods, and multi-level modeling.