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Session Submission Type: Full Paper Panel
This APSA panel delves into the innovative intersection of text analysis, Large Language Models (LLMs), Natural Language Processing (NLP), and political science, showcasing recent advancements and applications in understanding political phenomena. This session presents a series of papers that leverage NLP techniques to extract, analyze, and interpret political language, offering novel insights into the ideological underpinnings, populist rhetoric, and democratic commitments of political leaders. Humeyra Biricik of Oxford University applies LLM analysis to predict democratic backsliding from political speeches, while Siyu Liang of UCLA addresses data scarcity in stance detection by focusing on cross-domain transfer learning. Allison Koh of King’s College London investigates the influence of state actors on social media platforms in the wake of policy changes. Patrick Y. Wu of NYU and his team use generative Large Language Models to analyze moralization and othering in social media content. Lastly, Paulina Garcia Corral of Hertie School leverages NLP to extract and synthesize causal arguments from political texts, offering new insights into political narratives. By blending theoretical insights with empirical research, this panel aims to bridge the gap between technical NLP methodologies and practical political science applications.
Political Speech as a Forecasting Tool for Democratic Backsliding - Humeyra Biricik, Oxford University
Cross-Domain Transfer Learning for Polarized Text - Siyu Liang, University of California, Los Angeles
Using Generative LLMs to Scale Othering and Moralization in Social Media Posts - Patrick Y. Wu, American University; Jonathan Nagler, New York University; Joshua A. Tucker, New York University; Solomon Messing, New York University
Do Authoritarian Regimes Thrive in Less Transparent Social Media Spaces? - Allison Wun-hui Koh, University of Birmingham
Extracting Causal Arguments from Political Text - Paulina Garcia Corral, Hertie School