Date of Award

6-9-2020

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Computer Science

First Advisor

Nitin Agarwal

Abstract

Online Social Networks (OSNs) are largely considered a powerful vehicle to disseminate information. It is common to see social media used for propaganda maneuvering, disinformation campaigns, and crowd manipulation. These platforms also provide a method to coordinate and influence behavior. Many crime organizations use social media to coordinate events and then quickly disperse making it difficult to identify the actors. The focus of this study is to advance our understanding of the use of collective action theory measurements in communication networks extracted from Deviant Cyber Flash Mobs (DCFMs). We assess the formation of these cyber collective groups by analyzing the timeseries development of an ISIS propaganda and recruiting network over its campaign lifecycle. This research will identify the socio-technical behaviors of a cyber collective group and models the input parameters of Control, Interest, and Power so that we can characterize and quantitatively measure the power signature of a collective-action group. After identifying the high-power users, we develop the communication network of the DCFM, and then ingest this network into the FSA’s bi-level optimization model, where we can identify the most influential collective-action user FSA sets. This research extends upon the recent work of identification of DCFMs into examining how DCFMs are used with the community detection method of Focal Structure Analysis (FSA) to identify powerful and influential groups of actors. This method provides powerful insights into the network structure of a DCFM. Using this method, we will identify, and more importantly show, how we can mitigate strong influential groups’ impacts on a network.

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