The Quantitative Social Science Reading Group is an approximately weekly gathering organized by Professor. Lai Wei from the Department of Sociology and Professor. Haohan Chen from the Department of Politics and Public Administration at the University of Hong Kong.
The reading group discusses recent papers on computational social science, broadly defined. Most of the papers are about either causal inference or NLP. The sessions are led by graduate students at HKU.
2024 Fall
(Coordinator: Yingqi Huang)
September 12, 2024: Embedding Regression: Models for Context-Specific Description and Inference
Rodriguze, P. L., Spirling, A., & Stewart, B. M. (2023). Embedding Regression: Models for Context-Specific Description and Inference. The American Political Science Review, 117(4), 1255–1274.
Presented by Xirui Gao: Presentation slides.
September 19, 2024: The Application of Large Language Model in Utterance-level Classification Task
Ziems, C., Held, W., Shaikh, O., Chen, J., Zhang, Z., & Yang, D. (2024). Can Large Language Models Transform Computational Social Science? Computational Linguistics, 50(1), 237–291.
Presented by Tianyi Jessie Wu: Presentation slides.
September 26, 2024: Defining the Target Quantity Connects Statistical Evidence to Theory
Lundberg, I., Johnson, R., & Stewart, B. M. (2021). What Is Your Estimand? Defining the Target Quantity Connects Statistical Evidence to Theory. American Sociological Review, 86(3), 532–565.
Presented by Yuyao Liu: Presentation slides.
October 3, 2024: Matching Methods for Causal Inference with Time-Series Cross-Sectional Data
Imai, K., Kim, I. S., & Wang, E. H. (2023). Matching Methods for Causal Inference with Time‐Series Cross‐Sectional Data. American Journal of Political Science, 67(3), 587–605.
Presented by Linghan Ge: Presentation slides.
October 10, 2024: How to make causal inferences using texts
Egami, N., Fong, C. J., Grimmer, J., Roberts, M. E., & Stewart, B. M. (2022). How to make causal inferences using texts. Science Advances, 8(42), eabg2652–eabg2652.
Presented by Haohan Lily Hu: Presentation slides.
October 24, 2024: Estimating the Ideology of Political YouTube Videos
Lai, A., Brown, M. A., Bisbee, J., Tucker, J. A., Nagler, J., & Bonneau, R. (2024). Estimating the Ideology of Political YouTube Videos. Political Analysis, 32(3), 345–360.
Presented by Baichen Du: Presentation slides.
October 31, 2024: Theoretical Foundations and Limits of Word Embeddings
Arseniev-Koehler, A. (2024). Theoretical Foundations and Limits of Word Embeddings: What Types of Meaning can They Capture? Sociological Methods & Research, 53(4), 1753–1793.
Presented by Miner Ye: Presentation slides.
November 7, 2024: A framework for Quantifying Individual and Collective Common Sense
Whiting, M. E., & Watts, D. J. (2024). A framework for quantifying individual and collective common sense. Proceedings of the National Academy of Sciences - PNAS, 121(4), e2309535121-.
Presented by Ruomeng Liu: Presentation slides.
November 14, 2024: Measuring Populism, Nationalism, and Authoritarianism in U.S. Presidential Campaigns with LLMs
Bonikowski, B., Luo, Y., & Stuhler, O. (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.
Presented by Fanglin Irene Liu: Presentation slides.
November 21, 2024: Automated Social Science: Language Models as Scientist and Subjects
Manning, B. S., Zhu, K., & Horton, J. J. (2024). Automated Social Science: Language Models as Scientist and Subjects.
Presented by Yifei Zhu: Presentation slides.
