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.

中文翻译：

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使用社交媒体数据识别飓风引发的基础设施破坏的多标签分类方法

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，例如要求在短时间内覆盖大面积区域，因此研究调查了社交传感在灾难后评估破坏/破坏的潜力。但是，先前的研究集中在确定社交媒体帖子是否与损害相关。因此，需要先进的方法来根据此类数据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，以考虑到发生破坏的情绪来确定多种类型的基础设施破坏的同时发生－无论帖子是否报告了实际的破坏（负面），或总体中断（中性），或不受中断影响（正）。此外，我们提出了一个动态映射框架，用于可视化基础架构中断。我们使用一种地理解析方法，该方法从社交媒体帖子的文本中提取位置。我们使用飓风艾玛（Irma）和迈克尔（Michael）收集的Twitter数据测试了提议的方法。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，基于特定关键字的与中断相关的推文不一定表示实际中断。许多推文代表一般性的对话，对潜在破坏的担忧以及对不受任何破坏影响的积极情绪。此外，动态中断图可能会显示县和点/坐标级别的中断。确定中断类型及其位置对于灾难恢复，响应和救济行动至关重要。通过推断多个中断的同时发生，所提出的方法可能有助于基础设施服务提供商和灾难管理组织之间的协调。