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检测应用程序流量的异常时空模式,以应对灾难性事件
Sofia Medina1, Shazia'Ayn Babul1, Timothy LaRock1
1Mathematical Institute, University of Oxford, Oxford, UK.
概括
手机数据揭示了在像圣母院大火这样的重大事件中信息如何传播. 使用模式,特别是在Twitter (X) 上,显示了从灾害地点的辐射传播,提供了对公众反应的见解.
科学领域:
- 计算社会科学 计算社会科学
- 网络科学 网络科学
- 数据科学数据科学数据科学
背景情况:
- 史无前例的事件造成了重大的社会干扰.
- 了解危机期间信息传播动态至关重要.
研究的目的:
- 为了揭示手机应用程序使用的模式和在应对灾难性事件时传播的信息.
- 为了分类空间和时间反应模式及其随时间的放松.
主要方法:
- 使用了NetMob2023数据挑战数据集 (移动应用程序流量量).
- 分析了2019年3月至5月的数据,重点关注圣母院火灾和里昂爆炸事件.
- 使用数据上传/下载量作为信息传输动态的代理.
主要成果:
- 在响应巴黎圣母院火灾时,识别了不同的信息传输动态集群.
- 观察到从圣母院地区的Twitter (X) 使用率高于基线的辐射传播.
- 在一次爆炸事件后,在里昂检测到类似的辐射图案.
结论:
- 开发了新的分析方法来追踪随时间推移的辐射信息传播模式.
- 展示了手机用户对意外灾难事件的反应的新视角.
- 提供了关于危机期间传播信息的时空动态的见解.
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