Network Science provides a framework to understand the large-scale discussions that happen on social media and their impact on society. However, a standard network model of a conversational network destroys the context that users are interacting …
If we want to model Twitter conversations with a network, we need to account for the context that users interact within. We propose a deep-learning approach to separating Twitter data out into contextualized networks. We then show that these contextualized networks have very different nodesets, topology, and central actors than observed in the non-contextualized networks. Our findings suggest that the dominant way of modeling social media conversations may be inaccurately portraying the nature of the conversations and the most important people in them.
Network Science provides a framework to understand the large-scale discussions that happen on social media and their impact on society. However, a standard network model of a conversational network destroys the context that users are interacting …
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 …
Modularity Vitality measures a node's contribution to group structure. In hashtag networks, then, Modularity Vitality can be used to select hashtag that contributes most to a topic found through community detection. We show that this leads to more interpretable topic analysis for a large Twitter dataset.
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.