Course Readings

[1] Bailey, Michael A., Anton Strezhnev and Erik Voeten (2017). Estimating Dynamic State Preferences from United Nations Voting Data. Journal of Conflict Resolution 61:430-456 (Ideal Point Estimation).
[2] Barbera, Pablo (2015). Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data. Political Analysis 23:76–91 (Bayesian Statistics, Ideal Point Estiation).
[3] Bisbee, James and Arthur Spirling (2026). What to do When Humans When Humans are No Longer the Gold Standard: Large Language Models, State of the Art, and Robustness. Working Paper (Text Analysis, Neural Networks).
[4] Clark, Tom S. and Benjamin Lauderdale (2010). Locating Supreme Court Opinions in Doctrine Space. American Journal of Political Science 54:871–890 (Bayesian Statistics, Ideal Point Estimation).
[5] Gentzkow, Matthew and Jesse M. Shapiro (2010). What Drives Media Slant? Evidence From U.S. Daily Newspapers. Econometrica 78:35–71 (Text Analysis, Supervised Learning).
[6] Goettler, Ronald L. and Ron Shachar (2001). Spatial Competition in the Network Television Industry. RAND Journal of Economics 32:624–656 (Ideal Point Estimation).
[7] Grimmer, Justin (2010). A Bayesian Hierarchical Topic Model for Political Texts: Measuring Expressed Agendas in Senate Press Releases. Political Analysis 18:1–35 (Text Analysis, Unsupervised Learning).
[8] Hopkins, Daniel J., Yphtach Lelkes, Samuel Wolken (2025). The Rise of and Demand for Identity-oriented Media Coverage. American Journal of Political Science 69:483––500 (Text Analysis, Neural Networks).
[9] Imai, Kosuke and Aaron Strauss (2011). Estimation of Heterogeneous Treatment Effects from Randomized Experiments, with Application to the Optimal Planning of the Get-Out-the-Vote Campaign. Political Analysis 19:1–19 (Nonparametrics).
[10] Lai, Angela, Megan A. Brown, James Bisbee, Joshua A. Tucker, Jonathan Nagler and Richard Bonneau (2024). Estimating the Ideology of Political YouTube Videos. Political Analysis 32:345––360 (Text Analysis, Neural Networks).
[11] Lax, Jeffrey R. and Justin H. Phillips (2009). How Should We Estimate Public Opinion in The States? American Journal of Political Science 53:107–121 (Bayesian Statistics).
[12] Martin, Gergory J. and Ali Yurukoglu (2017). Bias in Cable News: Persuasion and Polarization. American Economic Review 107:2565–2599 (Text Analysis, Supervised Learning).
[13] Mens, Gael Le and Aina Gallego (2025). Positioning Political Texts with Large Language Models by Asking and Averaging. Political Analysis 33:274–282 (Text Analysis, Neural Networks).
[14] Quinn, Kevin M., Burt L. Monroe, Michael Colaresi, Michael H. Crespin and Dragomir R. Radev (2010). How to Analyze Political Attention with Minimal Assumptions and Costs. American Journal of Political Science 54:209–228 (Text Analysis, Unsupervised Learning).
[15] Roberts, Margaret E., Brandon M. Stewart, Dustin Tingley, Christopher Lucas, Jetson Leder-Luis, Shana Kushner Gadarian, Bethany Albertson and David G. Rand (2014). Structural Topic Models for Open-Ended Survey Responses. American Journal of Political Science 58:1064–1082 (Text Analysis, Unsupervised Learning).
[16] Rodriguez, Pedro L., Arthur Spirling, and Brandom M. Stewart (2023). Embedding Regression: Models for Context-Specific Description and Inference American Political Science Review 117:1255-1274 (Text Analysis, Neural Networks).
[17] Silva, Bruno Castanho, Danielle Pullan and Jens Wackerle (2025). Blending in or Standing Out? Gendered Political Communication in 24 Democracies. American Journal of Political Science 69:653–668 (Text Analysis, Supervised Learning).
[18] Vishwanath, Arjun (2025). Race, Legislative Speech, and Symbolic Representation in Congress. American Journal of Political Science 69:578–593 (Text Analysis, Supervised Learning).