"Measuring Ideological Proportions in Political Speeches" with Yanchuan Sim, Justin H. Gross & Noah A. Smith. In Empirical Methods in Natural Language Processing
. 2013. abstract
| supplemental materials
We seek to measure political candidates' ideological positioning from their speeches. To accomplish this, we infer ideological cues from a corpus of political writings annotated with known ideologies. We then represent the speeches of U.S. presidential candidates as sequences of cues and lags (filler distinguished only by its length in words). We apply a domain-informed Bayesian HMM to infer the proportions of ideologies each candidate uses in each campaign. The results are validated against a set of preregistered, domain expert authored hypotheses.
"Emotional Responses to Disturbing Political News: The Role of Personality" with Timothy J. Ryan and Matthew S. Wells. Revise & Resubmit. abstract | slides
Recent scholarship in political science identifies emotions as an important antecedent to political behavior. Existing work, however, has focused much more on the political effects of emotions than on their causes. Here, we begin to examine how personality moderates emotional responses to political events. We hypothesized that the personality trait need for affect would moderate the emotions evoked by disturbing political news. Drawing data from a survey experiment conducted on a national sample, we find that individuals high in need for affect have an especially vivid emotional response to disturbing news—a moderating relationship that has the potential to surpass those associated with symbolic attachments.
"Evaluating Post-Primary Moderation: The Ideological Etch-a-Sketch" with Justin H. Gross, Amber Boydstun, Noah A. Smith and Yanchuan Sim. In Preparation. abstract | slides
Formal models of two-stage elections usually predict post-primary ideological moderation. According to the hypothesis, candidates face pressure to appeal to more extreme partisans during primary elections, and after securing the nomination should moderate their politics to appeal to the general electorate median voter. Motivated by this largely untested hypothesis, we test the theory using candidates' campaign speeches as data. We employ a two-stage text analysis model with presidential candidates' speeches from 2008 and 2012, in addition to qualitative evaluation for robustness. The results show that Barack Obama, John McCain and Mitt Romney did indeed make substantively significant shifts away from the ideological extremes after securing their parties' presidential nominations.
"The Cue-Lag Model for Ideological Proportions" with Justin H. Gross, Noah A. Smith and Yanchuan Sim. In Preparation. abstract | slides
Over the past decade, political researchers have increasingly recognized the promise of computational text analysis. Political drama often plays out via the written and spoken word, and the discipline has sought out methods for leveraging text to inform our empirical studies. Many richly nuanced constructs of direct interest to researchers, like political ideology, can be advanced by using data from political writings and speeches. Focusing on individual ideological expression in political rhetoric, we develop a model that integrates scholarly expertise with computational efficiency to extract ideological cues from text. Our approach involves two stages: First, we identify a vocabulary of n-grams that distinguish between sets of authors grouped by their ideological persuasion. Then we tailor a hidden Markov model to represent speakers as transitioning between latent ideological states manifested through the emission of cue-terms from the learned vocabulary. We conclude with several validation results and a discussion of possible applications of the model across discipline subfields and research questions.
"Citizen Evaluation of Ideological Text" with Michael B. MacKuen. In Preparation. Presented at MPSA 2015. abstract | slides
Prior work shows that elites use rhetoric strategically, sending ideological signals to voters through language. Do citizens understand those signals? Can subtle changes in language affect how citizens perceive candidate ideology? Using a battery of survey experiments, we present respondents with political messages, making only subtle changes to language. Results show that participants receiving more ideological language placed the source quite far from the ideological center. Using open-ended responses, we also assess the process by which citizens perceive the signals. While participants give heterogenous explanations for their assessments, different criteria do not seem to predict different placements of the source.
"Ideological Rhetoric" PoliInformatics Retreat. Depts. of Political Science, Computer Science, Statistics and Journalism. Iowa State University. May 2015. abstract | slides
The PoliInformatics group at Iowa State University is exploring new ways to marry machine learning to substantive questions about politics. They invited me, along with researchers from Harvard, Arizona State and the University of California at Irvine, to present our work and broader lessons about methods for computational social science. My talk focused on assessing features like 'ideology' in political texts.
"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.