Computational Social Science Forum — Privacy Between the Civil War and the Great Depression

CSS Training Program

October 12, 2021
4:00pm to 5:00pm
Virtual Participation


Computational Social Science Forum
Date: Tuesday, October 12, 2021
Time: 4:00-5:00 PM Pacific Time
Location: Virtual Participation – Register to attend via Zoom

Privacy Between the Civil War and the Great Depression

Speaker: Martin Eiermann, PhD candidate, Sociology, UC Berkeley
Abstract: Between 1870 and 1930, the logic of privacy emerged in the United States as a salient topic of political debate, jurisprudence, and public discourse. Using two different word embedding models, and drawing on a computational analysis of ~90,000 historical newspaper records, I examine shifts in the meaning and application of privacy over six decades. I show that the substantive meaning of privacy remained relatively stable but that the terminology of privacy began to infuse discussions of financial data, medical information, and telecommunications after the turn of the twentieth century. I also address three challenges of working with digitized text — data cleaning of OCR'ed data with varying quality; verification of results' robustness to different model specifications; and the integration of computational text analysis into a larger mixed-methods research project. 

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. 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. This Forum is organized as part of the Computational Social Science Training Program, and weekly meetings are hosted by researchers from BIDS and D-Lab. The group welcomes social scientists and 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. Interested UC Berkeley community members are invited to use this registration form to receive the schedule and access links. Please contact for more information or if you are interested in presenting current research for an upcoming session.


Martin Eiermann

PhD candidate, Sociology, UC Berkeley

Martin Eiermann is a PhD candidate in the Department of Sociology at U.C. Berkeley. His current research examines how the surveillance and classification of different populations are shaped by the institutional logics and street-level practices of specific state and non-state organizations. He integrates archival research designed to uncover intra-organizational decision-making with quantitative and computational approaches that capture longitudinal trends in large corpora of historical data and aggregate patterns across larger populations.