Online extremism is a growing and pernicious problem, and increasingly linked to real-world violence. We introduce a new resource to help research and understand it: ExtremeBB is a structured textual dataset containing nearly 44M posts made by more than 300K registered members on 12 different online extremist forums, enabling both qualitative and quantitative large-scale analyses of historical trends going back two decades. It enables us to trace the evolution of different strands of extremist ideology; to measure levels of toxicity while exploring and developing the tools to do so better; to track the relationships between online subcultures and external political movements such as MAGA and to explore links with misogyny and violence, including radicalisation and recruitment. To illustrate a few potential uses, we apply statistical and data-mining techniques to analyse the online extremist landscape in a variety of ways, from posting patterns through topic modelling to toxicity and the membership overlap across different communities. A picture emerges of communities working as support networks, with complex discussions over a wide variety of topics. The discussions of many topics show a level of disagreement which challenges the perception of homogeneity among these groups. These two features of mutual support and a wide range of attitudes lead us to suggest a more nuanced policy approach than simply shutting down these websites. Censorship might remove the support that lonely and troubled individuals are receiving, and fuel paranoid perceptions that the world is against them, though this must be balanced with other benefits of de-platforming. ExtremeBB can help develop a better understanding of these sub-cultures which may lead to more effective interventions; it also opens up the prospect of research to monitor the effectiveness of any interventions that are undertaken.
翻译:在线极端主义是一个日益严重和有害的问题,而且日益与现实世界暴力相关。我们引入了一种新的资源来帮助研究和理解它:极端BB是一个结构化的文本数据集,由超过300K的注册成员在12个不同的在线极端主义论坛上制作了近44M 个文本数据集,从质量和数量上对20年前的历史趋势进行大规模分析,使我们能够追踪极端主义意识形态不同部分的演变过程;测量毒性水平,同时探索和开发更好的工具;跟踪在线亚文化与外部政治运动(如MAGA)之间的关系,并探索与误解和暴力(包括激进化和招聘)的联系。为了展示一些潜在的用途,我们应用统计和数据挖掘技术来以各种方式分析网上极端主义的格局,从发布模式到毒性和不同社区的成员重叠。 社区作为支持网络开展工作,就广泛的议题展开复杂的讨论。 许多议题的讨论表明,对于这些团体之间对同性干预前景的认同程度可能形成挑战。 相互支持和广泛理解(包括激进化和招募)的两种特征,我们运用的统计和数据挖掘技术,以各种方式分析网上极端主义的面面面面面面分析,这或许会改变世界的面面面观,但是,也意味着,它们可能逐渐消除了这些极端主义,从而改变了这些观点。