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Studying politics and policy with large-scale data and computation
November 16, 2023 @ 1:00 pm - 2:30 pm
Serina Chang (Stanford University)
Millikan Room (E53-482)
The Department of Political Science welcomes
Job Candidate for the Shared Position in the Department of Political Science and the Schwarzman College of Computing
PhD Candidate, Stanford University
Presenting: Studying politics and policy with large-scale data and computation
Thursday, November 16th
From 1:00 pm to 2:30 pm
In the Millikan Room, E53-482
We hope to see you there!
Abstract: In this talk, I will discuss my work to leverage novel data sources and computational methods to derive new insights about policies, politics, and their intersection. I will focus on two examples of highly politicized policy domains in the US: COVID-19 pandemic response and immigration. First, I will describe our work on pandemic response, where we developed methods to infer fine-grained mobility networks from aggregated location data, with 5.4 billion hourly edges, and designed an epidemiological model that integrates these networks to simulate the spread of COVID-19 with unprecedented granularity. Our methods enable us to investigate contentious policy problems, such as reopening tradeoffs and socioeconomic disparities, and to examine geographic spillovers (through causal inference methods) that arise from lacking coordination between jurisdictions. Second, I will discuss our work that develops natural language processing methods to study the politics of immigration from 140 years of US political speeches. Our approach reveals that, while overall attitudes towards immigration are more positive than ever, the political parties have also become more polarized than ever on this issue, and negative attitudes today are characterized by language that echoes past exclusionary periods. This talk will focus on our Nature paper  and touch on related works in AAAI’23  and PNAS .
 Serina Chang*, Emma Pierson*, Pang Wei Koh*, Jaline Gerardin, Beth Redbird, David Grusky, and Jure Leskovec. Mobility network models of COVID-19 explain inequities and inform reopening. Nature 2021.
 Serina Chang, Damir Vrabac, Jure Leskovec, and Johan Ugander. Estimating geographic spillover effects of COVID-19 policies from large-scale mobility networks. AAAI 2023.
 Dallas Card, Serina Chang, Chris Becker, Julia Mendelsohn, Rob Voigt, Leah Boustan, Ran Abramitzky, and Dan Jurafsky. Computational analysis of 140 years of US political speeches reveals more positive but increasingly polarized framing of immigration. PNAS 2022.