A multilabel classification approach to identify hurricane‐induced infrastructure disruptions using social media data
A multilabel classification approach to identify hurricane‐induced infrastructure disruptions using social media data
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DOI：
10.1111/mice.12573
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发表时间：
2020-06
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影响因子：
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通讯作者：
K. Roy;Samiul Hasan;Pallab Mozumder
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文献类型：
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作者：
K. Roy;Samiul Hasan;Pallab Mozumder
在灾难期间快速识别基础设施中断在恢复和恢复操作中起着重要作用。由于使用物理传感技术的局限性，例如需要在短时间内覆盖大面积，研究调查了社会传感在灾难后进行损害/破坏评估的潜力。然而，以前的研究集中在确定社交媒体帖子是否与损害有关。因此，需要先进的方法来从这些数据中推断实际的基础设施中断及其位置。在本文中，我们提出了一种多标签分类方法来识别多种类型的基础设施中断的同时发生，考虑到对中断的情绪-无论是帖子报告实际中断（负面），还是一般中断（中性），还是不受中断影响（正面）。此外，我们提出了一个动态映射框架，可视化基础设施中断。我们使用地理解析方法从社交媒体帖子的文本中提取位置。我们测试所提出的方法使用Twitter的数据收集在飓风厄玛和迈克尔。所提出的多标签分类方法的性能优于基线方法（使用简单的关键字搜索和情感分析）。我们还发现，基于特定关键字的中断相关推文并不一定表明实际中断。许多推文代表了一般性的对话，对潜在中断的担忧，以及不受任何中断影响的积极情绪。此外，一个动态的中断地图显示县和点/坐标级中断的潜力。确定中断类型及其位置对于灾难恢复、响应和救援行动至关重要。通过推断多个中断的共同发生，所提出的方法可以帮助基础设施服务提供商和灾难管理组织之间的协调。
Rapid identification of infrastructure disruptions during a disaster plays an important role in restoration and recovery operations. Due to the limitations of using physical sensing technologies, such as the requirement to cover a large area in a short period of time, studies have investigated the potential of social sensing for damage/disruption assessment following a disaster. However, previous studies focused on identifying whether a social media post is damage related or not. Hence, advanced methods are needed to infer actual infrastructure disruptions and their locations from such data. In this paper, we present a multilabel classification approach to identify the co‐occurrence of multiple types of infrastructure disruptions considering the sentiment toward a disruption—whether a post is reporting an actual disruption (negative), or a disruption in general (neutral), or not affected by a disruption (positive). In addition, we propose a dynamic mapping framework for visualizing infrastructure disruptions. We use a geo‐parsing method that extracts location from the texts of a social media post. We test the proposed approach using Twitter data collected during hurricanes Irma and Michael. The proposed multilabel classification approach performs better than a baseline method (using simple keyword search and sentiment analysis). We also find that disruption‐related tweets, based on specific keywords, do not necessarily indicate an actual disruption. Many tweets represent general conversations, concerns about a potential disruption, and positive emotion for not being affected by any disruption. In addition, a dynamic disruption map has potential in showing county and point/coordinate level disruptions. Identifying disruption types and their locations is vital for disaster recovery, response, and relief actions. By inferring the co‐occurrence of multiple disruptions, the proposed approach may help coordinate among infrastructure service providers and disaster management organizations.