CHS: Medium: Hyperlocal and Hypertemporal Information in Mass Emergencies Events: Next Generation Crisis Informatics Data Collection and Analytics

CHS：中：大规模紧急事件中的超本地和超时间信息：下一代危机信息学数据收集和分析

• 批准号: 1564275
• 负责人: Kenneth Anderson
• 金额: $ 119.78万
• 依托单位: University of Colorado at Boulder
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1564275&HistoricalAwards=false
• 关键词: CHS; Medium; Hyperlocal; Hypertemporal; Information

This project will advance the empirical study of crisis informatics by moving the extensive analytics developed for post processing of social media-based crisis information to the point of data collection to create a set of intelligent data collection techniques. Crisis informatics is a multidisciplinary field combining human-computer interaction and social computing; its central tenet is that people use personal information and communication technology to respond to disaster in creative ways to cope with uncertainty. This research will reveal how people respond in disasters, both collectively and individually, by examining their digital traces. This will help create better emergency management practice and policy. The research will also advance the delivery of the information people need in disasters by leveraging the vast social media data sets that are produced during them. The safety of people and provision of emergency care as a function of improved situational awareness are the direct benefits of conducting this research.Past research shows that people local to an event seek and provide information online to help themselves and each other, and that this information is highly localized and temporalized when compared to the global conversation that otherwise arises. Current techniques to derive useful information from the social media stream are limited because they usually cannot scale to the vast growth in social media production; they are often restricted to examining only the language of social media posts and not their metadata; and social media are often collected in ways that prohibit the kinds of analyses that are needed for deep exploration and re-use of the data. This research relies on the body of work that the investigators have empirically conducted on the nature of social media behavior in disasters, and incorporates those rigorous deductive and inductive analytical methods at the point of data collection of the social media streams. This will enable intelligent data collection that can perform real-time analysis that bore in on the hypertemporal and hyperlocal features of information communicated via social media. With agility and rigor, solutions developed in this research will be able to pursue multiple threads of emergent concern that will spawn multiple simultaneous data collection trajectories. This will better enable real-time analysis and visualization of what is happening in the social media record during disaster events as they unfold.

该项目将推动危机信息学的实证研究，将基于社交媒体的危机信息后处理的广泛分析转移到数据收集点，以创建一套智能数据收集技术。危机信息学是人机交互与社会计算相结合的多学科领域；它的核心原则是，人们使用个人信息和通信技术，以创造性的方式应对灾难，以应对不确定性。这项研究将通过检查人们的数字痕迹，揭示人们在灾难中是如何集体和个人应对的。这将有助于建立更好的应急管理做法和政策。这项研究还将通过利用灾难期间产生的大量社会媒体数据集，促进人们在灾难中所需信息的传递。开展这项研究的直接好处是，作为一种改善态势意识的功能，人们的安全和提供紧急护理。过去的研究表明，当地的人们在网上寻找和提供信息来帮助自己和彼此，与其他方式产生的全球对话相比，这些信息是高度本地化和临时性的。目前从社交媒体流中获取有用信息的技术是有限的，因为它们通常无法适应社交媒体生产的巨大增长；他们通常仅限于检查社交媒体帖子的语言，而不是它们的元数据；社交媒体的收集方式往往禁止对数据进行深入探索和再利用所需的各种分析。本研究依赖于调查人员对灾难中社交媒体行为的本质进行的实证研究，并在社交媒体流的数据收集点上结合了这些严格的演绎和归纳分析方法。这将使智能数据收集成为可能，可以对通过社交媒体传播的信息的超时间和超本地特征进行实时分析。通过敏捷性和严密性，本研究中开发的解决方案将能够追求紧急关注的多个线程，从而产生多个同时的数据收集轨迹。这将更好地实现灾难事件发生时社交媒体记录中正在发生的实时分析和可视化。