Center for Informed Democracy and Social Cybersecurity (IDeaS) Seminar
- Newell-Simon Hall
- MICHAEL COLARESI
- William S. Dietrich II Professor of Political Science Research, and
- Academic Director, Pitt Cyber
- Department of Political Science, University of Pittsburgh
Does Not Compute (and Might Self-Destruct): Digital Vulnerabilities in the Liberal World Order
The alliances, cooperation, and coordination that comprise the liberal world order depend on sufficient public political support and trust within and across democracies. Unlike traditional gate-keeper modes of mass information transmission that have been active in liberal states for the last century, social networks, such as Twitter, Facebook, YouTube, WhatsApp and others, directly connect the mass attention of democratic citizens to content generated and propagated from potentially adversarial users and bots around the world (Tucker, et al 2018). This new information environment has created the opportunity for countries such as Russia to pursue adversarial digital disinformation campaigns, exploiting the fact that democracies run on citizens' beliefs and perceptions. Despite the detection of these campaigns across developed democracies, there is currently limited systematic understanding of their motivations and effectiveness across individual, group, and societal scales.
In this talk, we present a new, cross-disciplinary, multi-level theory of adversarial disinformation campaigns motivations and influence. We explain how these campaigns have previously been misunderstood and mis-measured in political science and present a new agent-based modeling architecture that allows the simulation of different platform rules and agent types (citizen, expert, and adversary) with varying levels of sophistication. Simulations clarify the vulnerability of democratic publics to adversarial disruption campaigns, particularly when adversaries have access to high resolution surveillance of citizens prior beliefs and prejudices. In our larger project, we are undertaking experimental and observational work to improve our testing suite and produce both educational and algorithmic solutions to mitigate the influence of disinformation campaigns (Park and Colaresi 2017). Without fundamental changes and investments to improve the information-architecture in democracies, our work is pessimistic about the stability of democratic cooperation in the digital age. One kernel of hope resides in the relative resilience of expert-knowledge on socially-edited, non-profit platforms such as Wikipedia, in comparison to the ad-based, solo-authored networks.
Michael Colaresi is the William S. Dietrich II Professor of Political Science, founding Director of Text(Col)Lab, and Research and Academic Director at the Institute for Cyber Law, Policy and Security at the University of Pittsburgh. His work leverages the accelerating availability of computational tools, including machine learning and Bayesian approaches, along with unstructured information, such as from digitized text, to build and improve models of national security secrecy and oversight, foreign influence campaigns, patterns of violence, and changes in human rights over time. He also develops computational and visual tools that enable domain specialists to work alongside computer scientists to improve specific applications. His work has been funded through several NSF grants and he was co-recipient of the Best Visualization Award from the Journal of Peace Research in 2017 and the Gosnell Prize for Excellence in Political Methodology from the Methodology section of the American Political Science Association in 2006. His most recent book, Democracy Declassified: The Secrecy Dilemma in National Security (Oxford University Press), explores the effectiveness of legislative oversight, freedom of information laws, and the press in resolving the inherent contradiction between domestic accountability and private information in liberal states. Democracy Declassified was shortlisted for the 2015 Conflict Research Society book prize. In his previous position at Michigan State University, he founded and directed the Social Science Data Analytics initiative.