Understanding the societal impacts caused by community disruptions (e.g., power outages and road closures), particularly during the response stage, with timeliness and sufficient detail is an underexplored, yet important, consideration. It is critical for effective decision‐making and coordination in disaster response and relief activities as well as post‐disaster virtual reconnaissance activities. This study proposes a semiautomated social media analytics approach for social sensing of Disaster Impacts and Societal Considerations (SocialDISC). This approach addresses two limitations of existing social media analytics approaches: lacking adaptability to the need of different analyzers or different disasters and missing the information related to subjective feelings, emotions, and opinions of the people. SocialDISC labels and clusters social media posts in each disruption category to facilitate scanning by analyzers. Analyzers, in this paper, are persons who acquire social impact information from social media data (e.g., infrastructure management personnel, volunteers, researchers from academia, and some residents impacted by the disaster). Furthermore, SocialDISC enables analyzers to quickly parse topics and emotion signals of each subevent to assess the societal impacts caused by disruption events. To demonstrate the performance of SocialDISC, the authors proposed a case study based on Hurricane Harvey, one of the costliest disasters in U.S. history, and analyzed the disruptions and corresponding societal impacts in different aspects. The analysis result shows that Houstonians suffered greatly from flooded houses, lack of access to food and water, and power outages. SocialDISC can foster an understanding of the relationship between disruptions of infrastructures and societal impacts, expectations of the public when facing disasters, and infrastructure interdependency and cascading failures. SocialDISC's provision of timely information about the societal impacts of people may help disaster response decision‐making.

中文翻译：

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半自动化的社交媒体分析，可感知灾难期间由于社区中断而造成的社会影响

了解社区干扰（例如，停电和道路封闭）所造成的社会影响，尤其是在响应阶段，要及时且足够详细，这是一个尚待探索但重要的考虑。对于在灾难响应和救灾活动以及灾后虚拟侦察活动中进行有效的决策和协调至关重要。这项研究提出了一种半自动化的社交媒体分析方法，用于对灾害影响和社会考虑因素（SocialDISC）进行社会感知。该方法解决了现有社交媒体分析方法的两个局限性：缺乏对不同分析人员或不同灾难的需求的适应性，以及缺少与人们的主观感受，情感和观点相关的信息。SocialDISC在每个中断类别中标记和聚集社交媒体帖子，以方便分析人员进行扫描。本文中的分析人员是从社交媒体数据中获取社会影响信息的人员（例如，基础设施管理人员，志愿者，学术界的研究人员以及受灾影响的某些居民）。此外，SocialDISC使分析人员能够快速解析每个子事件的主题和情感信号，以评估由破坏事件引起的社会影响。为了证明SocialDISC的性能，作者提出了一个基于美国历史上最昂贵的灾难之一的哈维飓风的案例研究，并分析了各个方面的破坏和相应的社会影响。分析结果表明，休斯敦人遭受房屋水灾的严重影响，缺乏食物和水以及停电。SocialDISC可以增进对基础设施中断与社会影响之间的关系，面对灾难时公众的期望以及基础设施相互依存和连锁故障的理解。SocialDISC提供有关人们的社会影响的及时信息可能有助于灾难响应的决策。