{"title":"Identifying Behavioral Factors Leading to Differential Polarization Effects of Adversarial Botnets","authors":"Yeonjung Lee, M. Ozer, S. Corman, H. Davulcu","doi":"10.1145/3610409.3610412","DOIUrl":null,"url":null,"abstract":"In this paper, we utilize a Twitter dataset collected between December 8, 2021 and February 18, 2022, during the lead-up to the 2022 Russian invasion of Ukraine. Our aim is 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. To understand the factors leading to these different effects, we analyze the interactions between the botnets and the users, distinguishing between barrier-crossing users, who navigate across different political camps, and barrier-bound users, who remain within their own camps. We observe that the pro-Russian botnet amplifies the barrier-bound partisan users within their own camp most of the time. In contrast, the pro-Ukrainian botnet amplifies the barrier-crossing users on their own camp alongside themselves for the majority of the time.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":null,"pages":null},"PeriodicalIF":0.4000,"publicationDate":"2023-06-01","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/10.1145/3610409.3610412","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 utilize a Twitter dataset collected between December 8, 2021 and February 18, 2022, during the lead-up to the 2022 Russian invasion of Ukraine. Our aim is 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. To understand the factors leading to these different effects, we analyze the interactions between the botnets and the users, distinguishing between barrier-crossing users, who navigate across different political camps, and barrier-bound users, who remain within their own camps. We observe that the pro-Russian botnet amplifies the barrier-bound partisan users within their own camp most of the time. In contrast, the pro-Ukrainian botnet amplifies the barrier-crossing users on their own camp alongside themselves for the majority of the time.