{"title":"Twitter上对抗性僵尸网络极化效应的检测和测量","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":"{\"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}","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}
Detecting and Measuring the Polarization Effects of Adversarial Botnets on Twitter
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.