Essays on the Measurement of Online Behavior
Dissertation defense
Stefan McCabe
Network Science PhD Candidate
Past Talk
Hybrid talk
Thursday
Oct 20, 2022
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1:00 pm
177 Huntington Ave
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11th floor
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The emergence of social media has changed many aspects of society, but one of the most stark changes has been the transformation of the social sciences from a data-poor field of research into one flooded with data. Revolutions in computation, digital storage, and connectivity have produced large, centralized warehouses of data on human behavior. With the advent of online social media, it became possible to store previously unimaginable amounts of behavioral data on individuals and make this data accessible to a wide array of social scientists. However, the sudden flood of data has not always led to the construction of meaningful measures of social behavior. This dissertation focuses on techniques for effectively using large-scale digital trace data to study American political behavior.

I begin by discussing how record linkage of social media and administrative data can support large-scale analyses of online behavior at scale, with modest bias (Chapter 1). I demonstrate the utility of this approach by applying the linked social media data to study the effect of Twitter's deplatforming of right-wing extremist accounts following the events of January 6, 2021 (Chapter 2). I next turn to concerns regarding the measurement of news sharing, arguing that conflicting results in the literature on ``filter bubbles'' is due to a lack of clarity on when domain-level analyses are preferable to URL-level ones (Chapter 3). Showing how such data can be used to advance the study of descriptive representation, I next present an analysis of gender, racial, and class disparities in the propensity of Twitter users to follow their own Member of Congress (Chapter 4). Finally, I present an update of a now-classic method for estimating Twitter users' ideal points from following behavior, emphasizing their dynamic nature and the importance of measures following the political context in which they are used (Chapter 5).

Dissertation Committee:

  • David Lazer (Chair), University Distinguished Professor, Department Political Science and Khoury College of Computer Sciences, Northeastern University
  • Nick Beauchamp, Associate Professor, Department of Political Science, Northeastern University
  • Katherine Haenschen, Assistant Professor, Departments of Communication Studies and Political Science, Northeastern University
  • Kevin Esterling, Professor, School of Public Policy and Department of Political Science, University of California - Riverside

About the speaker
About the speaker

Stefan is a sixth-year doctoral candidate at Northeastern University’s Network Science Institute, advised by David Lazer. His research focuses on developing methods for accurately measuring and describing online social behaviors. Before joining Northeastern, he was a graduate student at George Mason University’s Department of Computational Social Science, where he was advised by Rob Axtell and completed a master’s thesis on agent-based modeling methodologies. In Summer 2020, he was a research intern at Microsoft Research, working with David Rothschild.