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.
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.