Mapping the Russian Internet Troll Network on Twitter using a Predictive Model
This work addresses the threat of disinformation spread by Russian trolls on social media, offering a tool for network mapping, but it is incremental as it builds on existing data and methods.
The researchers tackled the problem of identifying Russian Internet trolls on Twitter by developing a predictive model that classifies accounts based on authenticity, achieving 88% prediction accuracy and over 90% similarity and correspondence in validation with known datasets.
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.