Graduate Student Posters

Estimating the Dark Figure of Crime Using Bayesian Additive Regression Trees Plus Poststratification (BARP)

Isabel Laterzo (University of North Carolina, Chapel Hill)

Abstract: Studies of both crime victimization and violence often suffer from demonstrably unreliable crime figures. Consequently, researchers typically use homicide rates as an indicator to reflect all types of violence, despite this figure’s biases. The...

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Framing Democracy: Identifying Autocratic Anti-Democratic Propaganda Using Word Embeddings

Patrick Chester (New York University)

Abstract: There is substantial empirical evidence that indicates that democracy can spread between countries through observational learning. But do autocracies try to bias learning against...

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Estimating Heterogeneous Effect on Clustered Data Using Mixed-Effects Model

Junlong Zhou (New York University)

Abstract: Estimating the heterogeneous treatment effect is essential to assess the generality and mechanism of randomized experiments. In this paper, we propose an extension of the regression forest combining mixed-effects to analyze heterogeneous treatment effect using aggregated data sets. We show that including mixed-effects can improve estimation by accounting for the cluster-level...

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Decoding Propaganda Slogans in China: Reading Between the Lines Using Word Embeddings

Yin Yuan (University of California, San Diego)

Abstract: Propaganda slogans in China (a.k.a. “catchphrases” or “tifa”) are widely believed to be artifacts of propaganda aimed at indoctrinating the general public that convey little substantive political or policy information. This paper intends to show instead that these...

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Attributable Risk of Race: Detecting Partisan and Racial Gerrymandering

Sidak Yntiso and Sanford Gordon (New York University)

Abstract: How can we measure racial gerrymandering? Isolating racially disparate impacts of redistricting has proven difficult as sophisticated mapmakers can...

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Religiosity and Secularism: A Text-as-Data Approach to Recover Jihadist Groups' Rhetorical Strategies

Luwei Ying (Washington University in St. Louis)

*Award for Best Graduate Student Poster - Applications*

Abstract: Radical Islamists as the major force of the current "wave" of terrorism pursue impact, not only attacks. Scholars, however, for decades have almost exclusively focused...

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How a Deep Neural Network Contributes to Learning Causal Graph and Forecasting Political Dynamics

Seo Eun Yang (Ohio State University)

Abstract: Nonlinearity has been considerably interested in time series analysis of conflict/opinion dynamics. However, handling unknown nonlinear interactions on time series data is a methodologically challenging task because traditional models such as VAR Granger analysis or B-SVAR...

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Estimating Population Quantities From Multiple Data Sources Using the Structural Tensor Factorization

Soichiro Yamauchi (Harvard University)

Abstract: Estimating population quantities such as public opinions from survey data is a fundamental task in many social science studies. In political science, there is a growing interest in estimating public opinions at the level smaller than the entire nation, such as states (Lax and Phillips...

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Measuring Political Elite Networks With Wikidata

Omer Faruk Yalcin (Pennsylvania State University)

Abstract: An important issue in the study of comparative political elite networks is the elusiveness of cross-country empirical measurement. Most studies of political elites focus on country or region-specific institutions and use ad-hoc data collection methods like surveys...

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Gaussian Process Models for Causal Inference With Time-Series Cross-Sectional Data

Nuannuan Xiang and Kevin Quinn (University of Michigan)

*Award for Best Graduate Student Poster - Methods*

Abstract: In this paper, we develop a class of Gaussian Process models to estimate treatment effects with time-series cross-sectional data, in which a subset of units...

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Joint Image-Text Classification Using a Transformer-Based Architecture

Patrick Wu and Walter R. Mebane Jr. (University of Michigan)

Abstract: The use of social media data in political science is now commonplace. Social media posts such as Tweets are usually multimodal, comprising...

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Causal Inference Under Temporal and Spatial Interference

Ye Wang (New York University)

Abstract: Many social events and policies generate spillover effects in both time and space. Their occurrence influences not only the outcomes of interest in the future, but also these outcomes in nearby areas. In this paper, I propose a semi-parametric approach to estimate the direct and indirect/...

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Supervised Learning Election Forensics With Multi-Agent Simulated Training Data

Fabricio Vasselai (University of Michigan)

Abstract: The main advantage of using Supervised Machine Learning (SML) techniques to detect election fraud would be resorting to model-free or model-ensemble approaches, instead of usual...

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Rigorous Subjectivity: Demystifying and Improving Human Coding With Statistical Models

Matthew Tyler (Stanford University)

Abstract: Researchers are often tasked with applying subjective or contested labels to objects such as text and images. For example, researchers might hire coders to label the ideological slant of news articles. I show how two typical coding workflows in political science, traditional small-team...

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A Win or a Flop? Identifying and Estimating Unintended Protest Costs in Measuring Success Outcomes

Kimberly Turner (Southern Illinois University, Carbondale)

Abstract: How we measure protest success and how to identify and measure the lagged effects of movements has long besieged the field. Estimating the full impact of a movement, both its intended and unintended consequences, is undermined by the lack of consensus on...

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