title: Detecting and Measuring the Polarization Effects of Adversarial Botnets on Twitter
abstract: In this paper we use a Twitter dataset collected between December 8, 2021 and February 18, 2022 towards the 2022 Russian invasion of Ukraine to design a data processing pipeline featuring a high accuracy Graph Convolutional Network (GCN) based political camp classifier, a botnet detection algorithm and a robust measure of botnet effects. Our experiments reveal that while the pro-Russian botnet contributes significantly to network polarization, the pro-Ukrainian botnet contributes with moderating