识别导致敌对僵尸网络差异极化效应的行为因素

IF 0.4 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS Applied Computing Review Pub Date : 2023-06-01 DOI:10.1145/3610409.3610412
Yeonjung Lee, M. Ozer, S. Corman, H. Davulcu
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引用次数: 0

摘要

在本文中,我们利用了2021年12月8日至2022年2月18日期间收集的推特数据集,该数据集是在2022年俄罗斯入侵乌克兰之前收集的。我们的目标是设计一个数据处理管道,该管道具有基于高精度图卷积网络(GCN)的政治阵营分类器、僵尸网络检测算法和僵尸网络效应的稳健度量。我们的实验表明,虽然亲俄罗斯的僵尸网络对网络两极分化有很大贡献,但亲乌克兰的僵尸网络有缓和作用。为了理解导致这些不同影响的因素,我们分析了僵尸网络和用户之间的互动,区分了跨越不同政治阵营的障碍用户和留在自己阵营中的障碍用户。我们观察到,亲俄罗斯的僵尸网络在大多数时候都会放大自己阵营中的党派用户。相比之下,亲乌克兰的僵尸网络在大多数情况下都会放大自己阵营中的跨国界用户。
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Identifying Behavioral Factors Leading to Differential Polarization Effects of Adversarial Botnets
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.
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来源期刊
Applied Computing Review
Applied Computing Review COMPUTER SCIENCE, INFORMATION SYSTEMS-
自引率
40.00%
发文量
8
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