由跨国跨议题影响网络助长的城市骚乱
City riots fed by transnational and trans-topic web-of-influence
AI总结：
本研究针对2024年英国多城突发骚乱问题，通过多平台数据分析发现骚乱前已存在跨国跨议题的线上仇恨极端主义影响网络，揭示其韧性成因，并提出跨层级协作的应对方向。
摘要：
2024年夏季，英国各地原本并无关联的城市突发大规模骚乱，这令政府官员与民众都深感震惊。无论这些骚乱的具体导火索是什么，一个核心问题是：如何通过某种前兆行为预判这类大范围城市骚乱的爆发潜力，这类前兆行为能够标志着大规模骚乱意愿正在形成。\n我们在此展示的证据表明，在骚乱发生前很久，仇恨/极端主义社群的多平台生态中就已大规模出现特定的线上行为。我们对详细多平台数据的分析揭示，骚乱发生前早已存在一张影响网络，涉及地方、全国和全球层面的线上仇恨与极端主义社群。这张影响网络主要通过视频平台为各城市的潜在骚乱者提供煽动支持。\n该影响网络具备持续的韧性，因此未来仍将构成重大的地方、国家及国际威胁——原因在于它能在区域-国家-国际层面以及移民等议题间形成反馈循环，且它使用多个知名度较低的平台，超出了任何单一政府或平台的管控范围。\n展望未来，我们的研究结果表明，如果城市管理者跨越地方-国家-国际层级展开协作，就能像我们在本研究中所做的那样绘制出这一威胁图谱，并启动协商项目，或许可借助基于AI的自动化技术，大规模缓和这类预先存在的极端倾向。
英文摘要：
The sudden emergence of large-scale riots in otherwise unconnected cities across the UK in summer 2024 came as a shock for both government officials and citizens. Irrespective of these riots' specific trigger, a key question is how the capacity for such widespread city rioting might be foreseen through some precursor behavior that flags an emerging appetite for such rioting at scale. Here we show evidence that points toward particular online behavior which developed at scale well ahead of the riots, across the multi-platform landscape of hate/extremist communities. Our analysis of detailed multi-platform data reveals a web-of-influence that existed well before the riots, involving online hate and extremism communities locally, nationally, and globally. This web-of-influence fed would-be rioters in each city mainly through video platforms. This web-of-influence has a persistent resilience -- and hence still represents a significant local, national, and international threat in the future -- because of its feedback across regional-national-international scales and across topics such as immigration; and its use of multiple lesser-known platforms that put it beyond any single government or platform's reach. Going forward, our findings mean that if city administrators coordinate with each other across local-national-international divides, they can map this threat as we have done here and initiate deliberation programs that might then soften such pre-existing extremes at scale, perhaps using automated AI-based technology.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
