Machine learning (ML) is sometimes treated as synonymous with “artificial intelligence.” I myself wouldn’t go that far—not unless you are willing to conceptualize OLS regression as a form of artificial intelligence (we’ll talk more about that in the Nov. 6 meeting). A lot of what we’ve already talked about—especially topic modeling and word embeddings—is classified under the heading of unsupervised machine learning.
That said, ML is certainly a critical component of the modern (generative) AI landscape. You’ll see that more clearly in the Nov. 13 class when we talk about the Transformer architecture—the bedrock for cutting-edge text classification and large language models. Most of our discussions over the next couple weeks will deal specifically with supervised machine learning—or using labeled data to train models to predict unlabeled data.
- Required application reading (to be discussed in class): King, Gary, Jennifer Pan, and Margaret E. Roberts. 2013. “How Censorship in China Allows Government Criticism but Silences Collective Expression.” American Political Science Review 107(2):326-343.
- Exercise: TBD
- Recommended background reading:
- Molina, Mario and Filiz Garip. 2019. “Machine Learning for Sociology.” Annual Review of Sociology 45:27-45.
- Grimmer, Justin, Margaret E. Roberts, and Brandon M. Stewart. 2021. “Machine Learning for Social Science: An Agnostic Approach.” Annual Review of Political Science 24:395-419.
- Brand, Jeannie E., Xiang Zhou, and Yu Xie. 2023. “Recent Developments in Causal Inference and Machine Learning.” Annual Review of Sociology 49:81-110.
- Boelaert, Julien and Étienne Ollion. 2018. “The Great Regression: Machine Learning, Econometrics, and the Future of Quantitative Social Science.” Revue française de sociologie 59(3):475-506.
- Athey, Susan. 2017. “Beyond Prediction: Using Big Data for Policy Problems.” Science 355(6324):483-485.
- Mullainathan, Sendhil and Jann Spiess. 2017. “Machine Learning: An Applied Econometric Approach.” Journal of Economic Perspectives 31(2):87-106.
- Athey, Susan and Guido W. Imbens. 2019. “Machine Learning Methods that Economists Should Know About.” Annual Review of Economics 11:685-725.
- Grimmer, Justin and Brandon M. Stewart. 2013. “Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts.” Political Analysis 21(3):267-297.
- Grimmer, Justin. 2015. “We Are All Social Scientists Now: How Big Data, Machine Learning, and Causal Inference Work Together.” PS: Political Science & Politics 48(1):80-83.
- Shmueli, Galit and Otto R. Koppius. 2011. “Predictive Analytics in Information Systems Research.” MIS Quarterly 35(3):553-572.
- George, Gerry, Ernst Osinga, Dovev Lavie, and Brent Scott. 2016. “Big Data and Data Science Methods in Management Research.” Academy of Management Journal 59(5):1493-1507.
- Recommended application readings:
- Duxbury, Scott W. 2023. “A Threatening Tone: Homicide, Racial Threat Narratives, and the Historical Growth of Incarceration in the United States, 1926-2016.” Social Forces 102(2):561-585.
- Zheng, Haowen and Siwei Cheng. 2025. “Social Rigidity Across and Within Generations: A Predictive Approach.” Sociological Methods & Research 54(4):1683-1725.
- Hanna, Alex. 2013. “Computer-Aided Content Analysis of Digitally-Enabled Movements.” Mobilization 18(4):367-388.
- Kleinberg, Jon, Himabindu Lakkaraju, Jure Leskovec, Jens Ludwig, and Sendhil Mullainathan. 2017. “Human Decisions and Machine Predictions.” The Quarterly Journal of Economics 133(1):237-293.
- Choudhury, Prithwiraj, Dan Wang, Natalie A. Carlson, and Tarun Khanna. 2019. “Machine Learning Approaches to Facial and Text Analysis: Discovering CEO Oral Communication Styles.” Strategic Management Journal 40(11):1705-1732.
- Required reading (to be discussed in class): Siano, Frederico. 2025. “The News in Earnings Announcement Disclosures: Capturing Word Context Using LLM Methods.” Management Science 71(11):9831-9855.
- Exercise: TBD
- Recommended background reading:
- Vaswani, Ashish, et al. 2017. “Attention Is All You Need.” In NIPS’17: Proceedings of the 31st International Conference on Neural Information Processing Systems, edited by U. von Luxburg, I. Guyon, S. Bengio, H. Wallach, and R. Fergus. Long Beach, CA: Association for Computational Linguistics.
- Mostafavi, Moeen, Michael D. Porter, and Dawn T. Robinson. 2025. “Contextual Embeddings in Sociological Research: Expanding the Analysis of Sentiment and Social Dynamics.” Sociological Methodology 55(1):25-58.
- Wankmüller, Sandra. 2024. “Introduction to Neural Transfer Learning with Transformers for Social Science Text Analysis.” Sociological Methods & Research 53(4):1676-1752.
- Devlin, Jacob, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. “BERT: Pre-Training of Deep Bidirectional Transformers for Language Understanding.” In Proceedings of NAACL-HLT 2019, edited by J. Burstein, C. Doran, and T. Solorio. Minneapolis, MN: Association for Computational Linguistics.
- Martin, Louis, et al. 2020. “CamemBERT: A Tasty French Language Model.” In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, edited by D. Jurafsky, J. Chai, N. Schluter, and J. Tetreault. Online: Association for Computational Linguistics.
- Recommended application readings:
- Bonikowski, Bart, Yuchen Luo, and Oscar Stuhler. 2022. “Politics as Usual? Measuring Populism, Nationalism, and Authoritarianism in U.S. Presidential Campaigns (1952-2020) with Neural Language Models.” Sociological Methods & Research 51(4):1721-1787.
- Vicinanza, Paul, Amir Goldberg, and Sameer B. Srivastava. 2022. “A Deep-Learning Model of Prescient Ideas Demonstrates that They Emerge from the Periphery.” PNAS Nexus 2(1):1-11.
- van Loon, Austin, Sheridan Stewart, Brandon Waldon, Shrinidhi K. Lakshmikanth, Ishan Shah, Sharath Chandra Guntuku, Garrick Sherman, James Zou, and Johannes Eichstaedt. 2020. “Explaining the ‘Trump Gap’ in Social Distancing using COVID Discourse.” In Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020, edited by K. Verspoor, K. B. Cohen, M. Conway, B. de Brujin, M. Dredze, R. Mihalcea, and B. Wallace. Online: Association for Computational Linguistics.
- Moffitt, J. D., Catherine King, and Kathleen M. Carley. 2021. “Hunting Conspiracy Theories During the COVID-19 Pandemic.” Social Media + Society 7(3):1-17.