How Do Threats Induce Information Seeking?: When Natural Experiments Meet Text Data

For this first meeting of the Computational Social Science Forum, the group shared brief introductions and ideas for future sessions, followed by a talk/discussion led by Jae Yeon Kim (UC Berkeley) about his research with Andrew Thompson (University of Notre Dame) on natural experiments and text data.

Computational Social Science Forum
Date: Monday, September 14, 2020
Time: 12:00-1:30 PM Pacific Time
Location: Register to receive the schedule and access links.

How Do Threats Induce Information Seeking?: When Natural Experiments Meet Text Data

Jae Yeon Kim, UC Berkeley
Abstract: Many scholars have argued that threats activate the political interests of immigrant groups in the politics of their settled society. Yet, it is challengng to confirm how threat causally motivates political interests, in part due to many other potential variables that also influence an outcome. We address this problem by leveraging an exogenous shock to American politics (i.e., 9/11 attacks) and analyze immigrant political engagement. Specifically, we trace how this unexpected event increased the interests of Arab Americans, a direct target of xenophobia, and Indian Americans, an indirect target of xenophobia, in U.S. domestic politics. We classified Arab- and Indian-American newspapers using machine learning to demonstrate the substantial size of the change in the outcome between pre- and post-intervention periods. While the natural experiment design identifies the causal relationship between the intervention and the outcome variation, the multiple group comparison reassures the reliability of the observations. This project proposes one way to combine natural experiments and machine learning to identify a causal effect of an intervention. This research design can be easily transferred to other applied settings. In the research process, we also developed an accompanying R package that turns search results from one of the largest databases on ethnic newspapers and magazines published in the United States into a cleaned and wrangled dataset. Ref: Project git repository. Ref: How Threats Shape the Politics of Marginalized: Evidence from a Natural Experiment and Machine Learning, September 12, 2020, Jae Yeon Kim and Andrew Thompson.

The Computational Social Science Forum is an informal setting for the interdisciplinary exchange of ideas and scholarship at the intersection of social science and data science. Weekly meetings are hosted by researchers from BIDS and D-Lab, and participants engage in a variety of activities such as presentations of work in progress, discussions and critiques of recent papers, introductions to new tools and methods, discussions around ethics, fairness, inequality, and responsible conduct of research, as well as professional development. We welcome social scientists researchers with interests in data science methods and tools, and data scientists with applications or interests in public policy, social, behavioral, and health sciences. Participants include graduate students, postdocs, staff, and faculty, and members are encouraged to attend regularly in order to foster community around improving computational social science research, supporting the development and research of group members, and fostering new collaborations. This Forum is organized as part of the Computational Social Science Training Program. Meetings are currently held virtually on Mondays at 12:00-1:30 PM Pacific Time, and interested UC Berkeley community members are invited to use this registration form to receive the schedule and access links. Please contact BIDS Research Training Program Manager Adam Anderson for more information.