Today the borderless internet is used by state and non-state actors to manipulate information and societies in ways that were unheard of 50 years ago. Malicious actors can rapidly conduct information maneuvers with little cost at unprecedented scales to achieve far reaching consequences across the internet. They do this by exploiting features of the various social media platforms and the way humans naturally understand what they read and hear. These cyber-mediated threats to open and democratic societies have led to an emerging discipline known as social cybersecurity.

While various aspects of these campaigns have been explored,little research has focused on the campaign level of engagement. Our research seeks to answer the question: How can information warfare campaigns be identified and characterized quickly? Our goal is to 1) Improve understanding of information operations, and 2) Develop techniques to rapidly identify key factors such as bots and memes.

To accomplish this, I present the strategic context of the information warfare that we see today, and identify and define information warfare forms of maneuver . I develop various supervised and unsupervised methods to identify bots at four different data granularities. I present a deep learning model to classify memes as well as study the evolution of memes within a conversation. I present a template for understanding the major components of an information campaign and develop automatic ways to populate this template for a specific event. Finally, we present a Bot, Cyborg, and Troll Field Guide to help analysts and the general population understand these entities.

286 pages

Thesis Committee:
Kathleen M. Carley (Chair)
Douglas Sicker
Yulia Tsvetkov
Matthew Dabkowski (United States Military Academy)

James D. Herbsleb, Director, Institute for Software Research
Martial Hebert, Dean, School of Computer Science

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