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Text as Data in Social Science: Discovery, Measurement and Causal Inference
September 25, 2018 @ 4:00 pm - 5:00 pm
Brandon Stewart (Princeton University)
Social scientists are increasingly turning to computer-assisted text analysis as a way of understanding the digital footprints left by communities and individuals. Much of the technology that powers these approaches is borrowed from the fields of computer science and statistics; yet, social scientists have substantially different goals. We focus on the development of methods that support three core tasks: discovery, measurement and causal inference with text. We introduce the Structural Topic Model (STM), a bayesian generative model of text which is built for social science inference. Using this model as a running example, we will discuss the challenges of discovery, measurement and causal inference and how to adapt our tools to approach each task. The tasks will be illustrated with multiple examples across many different domains. The talk will end with future directions for this fast-moving, inter-disciplinary field. [Includes joint work with Molly Roberts, Justin Grimmer, Dustin Tingley, Edo Airoldi, Richard Nielsen and others.]
About the speaker: Brandon Stewart is an Assistant Professor in the Department of Sociology and is also affiliated with the Department of Politics and the Office of Population Research. He develops new quantitative statistical methods for applications across the social sciences. Methodologically his focus is in tools which facilitate automated text analysis and model complex heterogeneity in regression. Many recent applications of these methods have centered on using large corpora of text to better understand propaganda in contemporary China. His research has been published in journals such as American Journal of Political Science, Political Analysis and the Proceedings of the Association of Computational Linguistics. His work has won the Edward R Chase Dissertation Prize, the Gosnell Prize for Excellence in Political Methodology, and the Political Analysis Editor’s Choice Award.