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Identify the actors in the Russian Information Operation Networks and Three-Dimensional Geometric Visualization of Activities (305320)
*Sachith Eranga Dassanayaka, Texas Tech University
Ori Swed, Texas Tech University
Dimitri Volchenkov, Texas Tech University
Keywords: Predictive Model, AI, Geometry, Classification
Information operations by foreign adversaries pose a meaningful threat to democratic processes. Given the increased frequency of this type of threat, understanding those operations is paramount in the effort of combating their influence. Building on existing scholarship on the inner functions within those influence networks on social media, we suggest a new approach to map those types of operations. Using Twitter content identified as part of the Russian influence network, we created a predictive model to map the network operations. We classify accounts type based on their authenticity function for a sub-sample of accounts and trained AI to identify similar patterns of behavior across the network. Our model attains 88% prediction accuracy for the test set. We validate our predicted results set by comparing the similarities with the 3 million Russian troll tweets dataset. The result indicates 91% similarity between the two datasets. The predictive and validation results suggest that our neural network model can use to identify the tweets actors. The geometry of the network shows that there are noticeable isolations including activity trends regardless of the classification.