In this work we detail a scalable method of detecting groups of actors coordinating to exert influence on Twitter. Our method captures more coordinated behaviors than prior work and can detect coordination along multiple modalities. Looking a discussion of the Reopen America Protests, we find obvious, but non-threatening coordinated campaigns, as well a group of suspicious users promoting the protests in harmony across, each focusing on different state’s protests.
We show that modularity vitality, or the difference between the modularity of a graph with and without a node, can be used to measure that node's contribution to community structure. We also derive a scalable way of computing this for all nodes. We then show that this measure identifies nodes which are more important to network integrity than existing measures can. This method fragmentes the PA Road network over 8 times more effectively than previous methods.
We show that sequential voting on bills in Ukraine's legislature can be well modeled with a simple ODE. Our results imply that the first two votes are crucial for a bill's success. We also find that bills sponsored by the President exhibit quantitatively different behavior in that they are more sensitive to change between votes.
In this work we advocate for the use of interoperable pipelines for Social Cybersecurity. We demonstrate one such pipeline in the analysis of the Twitter discussion of the Trident Juncture exercise. We find bot activity aiming to discredit NATO targeted and allied nations.
We develop a procedure for finding time-segments of community stability in dynamic networks. This also functions as a community-based event detector. Applying this to the legislative voting network in Ukraine's 8th convocation, we identify the Euromaidan Revolution as a major event, and show that the network structure is vastly different before and after.