Oral Defense of Doctoral Dissertation: Brandon A. Shapiro
Apr 19, 2021, 1:30 - 3:30 PM
Oral Defense of Doctoral Dissertation
Doctor of Philosophy in Computational Social Science
Department of Computational and Data Sciences
College of Science
George Mason University
Brandon A. Shapiro
Bachelor of Science, University of Mary Washington, 2008
Master of Arts, Johns Hopkins University, 2012
Kinetic Action and Radicalization: Theory, Data, and Model
1:30 PM - 3:30 PM
Click here to join the event. All are invited to attend.
Andrew Crooks, Committee Chair
A. Trevor Thrall
ABSTRACT: Drone strikes appear to drive terrorist events with a lag that can be determined analytically. There is an ongoing debate as to the net value of drone strikes when all unintended consequences have been evaluated. Those in current and previous U.S. administrations have argued that the benefits of conducting drone strikes outweigh their costs. Others, however, have cited unintended consequences, such as collateral damage, which might muddy the waters when senior decisionmakers within the defense and intelligence communities decide to develop and execute various strategies to combat terrorism. Even previous U.S. administrations have questioned whether the Global War on Terrorism was successful in reducing the threat. A data-driven approach explores the relationship between drone strikes and subsequent responses—often in the form of terrorist attacks—carried out by those in the communities targeted by these counterterrorism measures. This research uses natural language processing, social network analysis, and other text analytic techniques to characterize the air and drone strike narrative reported by the news media. Leveraging evidence gleaned from that exploratory analysis, along with data collected from a variety of other sources, this research builds a computational agent-based model of opinion dynamics and applies it to the case study of Pakistan. Analysis of the drone strike campaign in Pakistan and terrorist attacks carried out in that country shows that the two series are highly correlated with a lag structure. Employing a simple model—which accounts for drone strikes in Pakistan and previously-conducted terrorist attacks in that country—explains nearly 100 percent of the variability of the terrorist attacks occurring in Pakistan during the entirety of the U.S. drone strike campaign. By using data from Pakistan to inform and build an agent-based model to simulate the dissemination of opinions through a notional terrorist network to generate terrorist attacks, which approximates the rate and magnitude observed in Pakistan from 2007 through 2018, this dissertation advances the field while at the same time laying the foundation for further work in the area of data-driven modeling.