{"title":"Detecting and Measuring the Polarization Effects of Adversarial Botnets on Twitter","authors":"Yeonjung Lee, M. Ozer, S. Corman, H. Davulcu","doi":"10.1145/3555776.3577730","DOIUrl":null,"url":null,"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 effects. In order to understand the factors leading to different effects, we analyze the interactions between the botnets and the barrier-crossing vs. barrier-bound users on their own camps. We observe that, where as the pro-Russian botnet amplifies barrier-bound partisan users on their own camp majority of the time, the pro-Ukrainian botnet amplifies barrier-crossing users on their own camp alongside themselves majority of the time.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":null,"pages":null},"PeriodicalIF":0.4000,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Computing Review","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3555776.3577730","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
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 effects. In order to understand the factors leading to different effects, we analyze the interactions between the botnets and the barrier-crossing vs. barrier-bound users on their own camps. We observe that, where as the pro-Russian botnet amplifies barrier-bound partisan users on their own camp majority of the time, the pro-Ukrainian botnet amplifies barrier-crossing users on their own camp alongside themselves majority of the time.