The expanding accessibility and appeal of investing have attracted millions of new retail investors. As such, investment discussion boards became the de facto communities where traders create, disseminate, and discuss investing ideas. These …
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.
The deep learning approach to graph classification is to embed nodes in a latent space, typically graph convolutions, and then to use these embeddings to make a single classification. The number of nodes may differ from one training example to the next, which poses a problem. We demonstrate that the node embedding distribution can be approximated using differentiable histograms. After the histograms are created, traditional convolutional layers can be used to classify the graph. This procedure leverages all available information, regardless of how the size of graphs vary. We demonstrate that this architecture gives incremental improvement for various benchmark datasets. We use this approach to classify bots on Twitter based on their communication graph. We find this classification technique generalizes better than previous methods, however sacrifices some precision.
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 …
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 …