Generative AI is fundamentally altering how we work, live, play, interact, and make decisions.
How we do science is no different. Indeed, most of the methodological advancements in contemporary CSS revolve in some way around the opportunities and dangers of using “GenAI” in social science research. We’ll discuss two ways computational social scientists are using GenAI as a research tool: using LLMs (decoder-only or encoder-decoder Transformer models) as classifiers and data simulators.
- Required application reading (to be discussed in class): Li, Linda, Orsolya Vásárhelyi, and Balázs Vedres. 2024. “Social Bots Spoil Activist Sentiment Without Eroding Engagement.” Scientific Reports 14(1):27005.
- Exercise: TBD
- Recommended background reading:
- Gilardi, Fabrizio, Meysam Alizadeh, and Maël Kubli. 2023. “ChatGPT Outperforms Crowd Workers for Text-Annotation Tasks.” Proceedings of the National Academy of Sciences 120(30):1-3.
- Chae, Youngjin and Thomas Davidson. 2026. “Large Language Models for Text Classification: From Zero-Shot Learning to Instruction-Tuning.” Sociological Methods & Research 55(2):501-567.
- Alvero, AJ, Dustin S. Stoltz, Oscar Stuhler, and Marshall A. Taylor 2026. “Generative AI in Sociological Research: State of the Discipline.” Sociological Science 13:45-62.
- Stoltz, Dustin S., Marshall A. Taylor, and Sanuj Kumar. 2026. “Selecting Language Models for Social Science: Start Small, Start Open, and Validate.” arXiv pre-print.
- Manning, Christopher D. 2022. “Human Language Understanding and Reasoning.” Dædalus 151(2):127-128.
- Waight, Hannah, Eddie Yang, Yin Yuan, Solomon Messing, Margaret E. Roberts, Brandon M. Stewart, and Joshua A. Tucker. 2026. “State Media Control Influences Large Language Models.” Nature 655:685-693.
- Recommended application readings:
- Weidmann, Nils B., Mats Faulborn, and David García. 2026. “Large Language Models Are Democracy Coders with Attitudes.” PS: Political Science & Politics 59(1):17-23.
- Sturgis, Patrick, Thomas R. Robinson, Laura Fung, and Caroline Roberts. 2026. “SOCbot: Using Large Language Models to Dynamically Measure and Classify Occupations in Surveys.” Sociological Methods & Research. OnlineFirst.
- Stoltz, Dustin S. 2025. “The Duality of Class and Love: Homogamy Spaces and the New York Social Elite, 1970-2020.” Poetics 113:1-16.
- Required application reading (to be discussed in class): Cao, Likun and Lianghao Dai. 2026. “Large Language Models as a Conduit for Value Shifts in Contemporary China.” Chinese Sociological Review. OnlineFirst.
- Exercise: TBD
- Recommended background reading:
- Boelaert, Julien, Samuel Coavoux, Étienne Ollion, Ivaylo Petev, and Patrick Präg. 2025. “Machine Bias. How do Generative Language Models Answer Opinion Polls?” Sociological Methods & Research 54(3):1156-1196.
- Bisbee, James, Joshua D. Clinton, Cassy Dorff, Brenton Kenkel, and Jennifer M. Larson. 2024. “Synthetic Replacements for Human Survey Data? The Perils of Large Language Models.” Political Analysis 32(4):401-416.
- Törnberg, Petter, Ola Söderström, Jennifer Barella, Saskia Greyling, and Sophie Oldfield. 2025. “Artificial Intelligence and the State: Seeing Like an Artificial Neural Network.” Big Data & Society 12(2):1-14.
- Manning, Benjamin S., Kehang Zhu, and John H. Horton. 2024. “Automated Social Science: Language Models as Scientists and Subjects.” NBER Working Paper.
- Kozlowski, Austin C. and James A. Evans. 2025. “Simulating Subjects: The Promise and Peril of Artificial Intelligence Stand-Ins for Social Agents and Interactions.” Sociological Methods & Research 54(3):1017-1073.
- Zhao, Xinrui Chloe, Douglas Guilbeault, and Amir Goldberg. 2026. “Free-form Association Tasks Reveal Stereotype Hallucination in Large Language Models.” arXiv pre-print.
- Ma, Xiangyu, Mengmi Zhang, Shannon Ang, and Minne Chen. 2026. “Not-quite-human Tastes: The Stylized Omnivorousness of LLM Survey Surrogates.” arXiv pre-print.
- Recommended application readings:
- Kozlowski, Austin C., Hyunku Kwon, and James A. Evans. 2024. “In Silico Sociology: Forecasting COVID-19 Polarization with Large Language Models.” arXiv pre-print.
- Gudiño, Jairo F., Umberto Grandi, and César Hidalgo. 2024. “Large Language Models (LLMs) as Agents for Augmented Democracy.” Philosophical Transactions A 382:1-17.
- Manivannan, Ajaykumar, Viktoria Spaiser, Tristan J. B. Cann, James Evans, Jordan P. Everall, Max Falkenberg, David Garcia, Weisi Guo, Rico Herzog, Ilona M. Otto, Yannick Oswald, Nicolò Pagan, Max Pellert, Charlie Pilgrim, Carlos Rodriguez-Pardo, Indira Sen, and Alexander Sasha Vezhnevets. 2026. “Generative AI for Climate Governance and Acceptability-Constrained Policy Design.” npj Climate Action 5(37):1-8.
- Bollen, Paige, Joe Higton, and Melissa Sands. 2026. “Nationally Representative, Locally Misaligned: The Biases of Generative Artificial Intelligence in Neighborhood Perception.” Political Analysis 34:479-487.