Keeping it Authentic: The Social Footprint of the Trolls Network

• URL: http://arxiv.org/abs/2409.07720v1
• Date: Thu, 12 Sep 2024 03:02:52 GMT
• Title: Keeping it Authentic: The Social Footprint of the Trolls Network
• Authors: Ori Swed, Sachith Dassanayaka, Dimitri Volchenkov,
• Abstract summary: In 2016, a network of social media accounts animated by Russian operatives attempted to divert political discourse within the American public around the presidential elections. We argue that pretending to be legitimate social actors obliges the network to adhere to social expectations. To test the robustness of this social footprint we train artificial intelligence to identify it and create a predictive model.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In 2016, a network of social media accounts animated by Russian operatives attempted to divert political discourse within the American public around the presidential elections. This was a coordinated effort, part of a Russian-led complex information operation. Utilizing the anonymity and outreach of social media platforms Russian operatives created an online astroturf that is in direct contact with regular Americans, promoting Russian agenda and goals. The elusiveness of this type of adversarial approach rendered security agencies helpless, stressing the unique challenges this type of intervention presents. Building on existing scholarship on the functions within influence networks on social media, we suggest a new approach to map those types of operations. We argue that pretending to be legitimate social actors obliges the network to adhere to social expectations, leaving a social footprint. To test the robustness of this social footprint we train artificial intelligence to identify it and create a predictive model. We use Twitter data identified as part of the Russian influence network for training the artificial intelligence and to test the prediction. Our model attains 88% prediction accuracy for the test set. Testing our prediction on two additional models results in 90.7% and 90.5% accuracy, validating our model. The predictive and validation results suggest that building a machine learning model around social functions within the Russian influence network can be used to map its actors and functions.

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