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 detail the challanges to performing coordination detecition on social media in a responsible way, and propose a framework to meet these challanges. The framework is then used to form a specific detector, which detects a number of coordinated campaigns in a real Twitter dataset.
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.
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.