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Journal of Vibration Testing and System Dynamics

C. Steve Suh (editor), Pawel Olejnik (editor),

Xianguo Tuo (editor)

Pawel Olejnik (editor)

Lodz University of Technology, Poland


C. Steve Suh (editor)

Texas A&M University, USA


Xiangguo Tuo (editor)

Sichuan University of Science and Engineering, China


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

Journal of Vibration Testing and System Dynamics 7(2) (2023) 113--128 | DOI:10.5890/JVTSD.2023.06.001

Sachith Dassanayaka$^{1}$, Ori Swed$^{2}$, Dimitri Volchenkov$^{1}$

$^{1}$ Department of Mathematics and Statistics, Texas Tech University, Lubbock, Texas, 79409-1042, USA

$^{2}$ Department of Sociology, Anthropology, and Social Work, Texas Tech University, Lubbock, Texas, 79409-1012, USA

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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. Building on existing scholarship on the inner functions within 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 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.


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