Mapping the Russian Internet Troll Network on Twitter using a Predictive Model

TL;DR

This paper develops a predictive model to identify and map Russian troll accounts on Twitter, achieving high accuracy and similarity with known datasets, aiding efforts to combat disinformation campaigns.

Contribution

The study introduces a novel predictive model that classifies Twitter accounts involved in Russian disinformation operations with 88% accuracy, validated against large datasets.

Findings

Model attains 88% prediction accuracy.

90.7% similarity with Russian troll dataset.

90.5% correspondence between predictions and actual categories.

Abstract

Russian Internet Trolls use fake personas to spread disinformation through multiple social media streams. Given the increased frequency of this threat across social media platforms, understanding those operations is paramount in combating their influence. 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 by introducing logical categories and training a predictive model to identify similar behavior patterns across the network. Our model attains 88% prediction accuracy for the test set. Validation is done by comparing the similarities with the 3 million Russian troll tweets dataset. The result indicates a 90.7% similarity between the two datasets. Furthermore, we compare our model predictions on a Russian tweets dataset, and the results state that there is 90.5% correspondence between the predictions and the actual categories. The prediction and validation results suggest that our predictive model can assist with mapping the actors in such networks.