Identifying Hurricane Evacuation Intent on Twitter
Identifying Hurricane Evacuation Intent on Twitter
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DOI：
10.1609/icwsm.v16i1.19320
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发表时间：
2022-05
期刊：
影响因子：
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通讯作者：
Xintian Li;Samiul Hasan;A. Culotta
中科院分区：
文献类型：
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作者：
Xintian Li;Samiul Hasan;A. Culotta
疏散对飓风期间拯救人的生命具有重大影响。然而，作为一个复杂的动态过程，它通常是很难知道个人的疏散决策实时。由于大量的信息通过社交媒体平台不断发布，我们可以利用它们来了解个人的疏散行为。在本文中，我们收集了2017年飓风伊尔玛期间的推文，并以主动学习的方式训练文本分类器，以区分表达积极疏散决定的推文与消极和不相关的推文。此外，我们进行人口统计分析和内容聚类，以调查疏散决策的潜在原因和相关因素。研究结果可用于帮助应急响应机构制定规划战略。
Evacuations have a significant impact on saving human lives during hurricanes. However, as a complex dynamic process, it is typically difficult to know individual evacuation decisions in real-time. Since a large amount of information is continuously posted through social media platforms, we can use them to understand individual evacuation behavior. In this paper, we collect tweets during Hurricane Irma in 2017 and train a text classifier in an active learning way to distinguish tweets expressing positive evacuation decisions from both negative and irrelevant ones. Additionally, we perform a demographic analysis and content clustering to investigate the potential causes and correlates of evacuation decisions. The results can be used to help inform planning strategies of emergency response agencies.