Machine Learning

The Many Dimensions of Political Discourse on Taiwan among Chinese netizens: an analysis of 20 million Weibo posts

Huan-Kai Tseng,  Osbern Huang, Waybe Lee and Yu-tzung Chang (National Taiwan University)

Abstract: Can microblog data be a useful substitute for internet poll to gauge public opinion on politically sensitive...

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What Matters to Voters? Examining Micro-Level and Macro-Level Drivers of Citizens' Economic and Political Evaluations

James Bisbee (Princeton University) and Jan Zilinsky (New York University)

Abstract: Voters form beliefs about the economy and politics on the basis of a potentially rich information set including experiences with own outcomes...

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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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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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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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Voter Turnout and Campaign Mail Features

Marcy Shieh and Blake Reynolds (University of Wisconsin-Madison)

Abstract:  The way images and text are presented to us can have a significant impact on how we are affected by the message contained in an advertisement. Therefore, we ask how does the formatting of campaign mail influence voter turnout? Using campaign mail from the 2018 primary and general elections in Texas, we examine the layout of campaign mailers. To do this, we leverage machine...

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Changing the Dialogue: Candidate Position-Taking in Primary Elections

Rachel Porter, Sarah Treul and Maura McDonald (University of North Carolina)

Abstract: The record-high number of women who ran for the U.S. Congress during the midterm elections led many journalists to...

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Measuring Political Polarization in Mass Publics: The Cluster-Polarization Coefficient

Isaac Mehlhaff (University of North Carolina, Chapel Hill)

Abstract: Political polarization has become a key concern in many important topics within comparative politics, yet past research has reached little consensus as to its substantive causes and effects. Much of this disagreement, I argue, stems from the use...

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Contrastive Multiple Component Analysis (cMCA): Applying the Contrastive Learning Method to Identify Political Subgroups

Tzu-Ping Liu and Takanori Fujiwara (University of California, Davis)

Abstract: Ideal point estimation and dimensionality reduction have long been utilized to simplify and cluster complex, high-dimensional political data (e.g., roll-call votes, surveys, and texts) for use in (preliminary) analysis and...

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Beyond Topics: Semi-Supervised Learning for Texts From a Measurement Perspective

Shiyao Liu (Massachusetts Institute of Technology)

Abstract: This project proposes a new methodological framework to use text data as a measurement in political science. Despite the abundance of text data available nowadays, conversion of text data into a measurement for a political concept remains a challenge that prevents...

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Extracting Political Events From Text Using Grammatical Dependency Parsing and Machine Learning

Andrew Halterman (Massachusetts Institute of Technology)

Abstract: This paper introduces a method that automatically extracts political events from text using grammatical parsing and machine learning. Much of the scientifically useful information about what political actors are doing is locked away in text. To extract this...

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Floor Speeches and Ideological Position: Estimating Ideology of Representatives

Benjamin Guinaudeau and Simon Roth (University of Konstanz)

Abstract: Estimating ideological position has always been challenging for political scientists. The technical progress of the last decades -digitalization, computational improvements- opened new opportunities to measure ideological position. While...

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Legislative Networks and Agenda-Setting in the UNGA and UNSC

Sabrina Arias and Robert Shaffer (University of Pennsylvania)

Abstract: How do the agendas of the United Nations Security Council (UNSC) and United Nations General Assembly (UNGA) influence each other? Which of these foundational UN institution leads, and which lags? How often do these chambers devote attention to...

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