Disrupting networks of hate: characterising hateful networks and removing critical nodes
Disrupting networks of hate: characterising hateful networks and removing critical nodes
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DOI：
10.1007/s13278-021-00818-z
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发表时间：
2022-12-01
影响因子：
2.8
通讯作者：
Giommoni, Luca
中科院分区：
文献类型：
--
作者：
Alorainy, Wafa;Burnap, Pete;Giommoni, Luca
心怀仇恨的个人和团体越来越多地利用互联网来表达他们的想法、传播他们的信仰和招募新成员。了解这些仇恨团体的网络特征有助于了解个人遭受仇恨的情况，并制定干预战略，通过破坏通信来减轻这些网络的危险。本文分析了两个仇恨的追随者网络和三个仇恨的Twitter用户转发网络，这些用户发布的内容随后被人类注释者归类为包含仇恨内容。我们的分析显示，仇恨的追随者网络之间以及仇恨的转发网络之间具有类似的连接特征。该研究表明，仇恨网络与其他“风险”网络相比，表现出更高的连通性特征，这可以被视为接触和传播在线仇恨的可能性。三个网络性能指标被用来量化仇恨内容的曝光和传染：巨型组件（GC）的大小，密度和平均最短路径。为了有效地识别节点的删除减少了网络中的仇恨流，我们提出了一系列结构化的节点删除策略，并测试其有效性。结果表明，删除用户的高度是最有效的减少仇恨的追随者网络连接（GC，大小和密度），从而降低暴露于网络仇恨的风险，并阻止其传播。
Hateful individuals and groups have increasingly been using the Internet to express their ideas, spread their beliefs and recruit new members. Understanding the network characteristics of these hateful groups could help understand individuals' exposure to hate and derive intervention strategies to mitigate the dangers of such networks by disrupting communications. This article analyses two hateful followers' networks and three hateful retweet networks of Twitter users who post content subsequently classified by human annotators as containing hateful content. Our analysis shows similar connectivity characteristics between the hateful followers networks and likewise between the hateful retweet networks. The study shows that the hateful networks exhibit higher connectivity characteristics when compared to other "risky" networks, which can be seen as a risk in terms of the likelihood of exposure to, and propagation of, online hate. Three network performance metrics are used to quantify the hateful content exposure and contagion: giant component (GC) size, density and average shortest path. In order to efficiently identify nodes whose removal reduced the flow of hate in a network, we propose a range of structured node-removal strategies and test their effectiveness. Results show that removing users with a high degree is most effective in reducing the hateful followers network connectivity (GC, size and density), and therefore reducing the risk of exposure to cyberhate and stemming its propagation.