We analyze three textual data streams to characterize the change that occurred during the Ukrainian revolution of 2014. These data streams include legislative bill text, posts on Ukrainian political blogs, and Twitter data. Each stream provides a …
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.
We identify time periods of disruption in the voting patterns of the Ukrainian parliament for the last three convocations. We compare two methods: ideal point estimation (PolSci) and faction detection (CS). Both methods identify the revolution in …
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.
In this study we compare two general methods of faction detection from Ukrainian Parliamentary roll call data, MacRae’s method and Gower’s method. Both methods were adapted to the special voting procedures and patterns of the Ukrainian Parliament, …
Current work in roll call modeling focuses on the legislative decision process and does not take advantage of the dynamic nature of legislation. Some political systems, such as Ukraine’s Verkovna Rada, are inherently dynamic, and should be modeled as …