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IBSS-L: Understanding Social and Geographical Disparities in Disaster Resilience Through the Use of Social Media

IBSS-L：通过使用社交媒体了解抗灾能力的社会和地理差异

基本信息

批准号：
1620451
负责人：
Nina Lam
金额：
$ 83.46万
依托单位：
Louisiana State University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2016
资助国家：
美国
起止时间：
2016-09-01 至 2021-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1620451&HistoricalAwards=false
关键词：
IBSS Understanding Social Geographical Disparities

项目摘要

This interdisciplinary research project will examine whether social and geographical disparities exist during the four phrases of emergency management (mitigation, preparedness, response, and recovery). The investigators will use multiple perspectives and scales to address the research questions, including analysis at the community, individual, and organizational scales. Findings from this project will provide valuable insights into the interplay among regional disparities, individual social networks and behavior, and governmental/organizational policies. This project will expand knowledge of whether social media use may serve to overcome or further deepen the social and geographical disparities in each phase of emergency management. The project will enhance understanding of how to conduct efficient mining of social media data in order to produce useful and valid scientific information, thereby advancing both social science and information science research by developing and testing algorithms that can be used to mine noisy and imperfect data from sources like Twitter. The knowledge gained from this project will help develop strategies to reduce disparities, create effective social media campaigns and emergency management outreach, and promote resilience to disasters. The methods used in this project will be applicable to study other disasters in other regions.Understanding the sources, patterns, and consequences of social and geographical disparities in disaster resilience is critical to building long-term resilient, healthy, and sustainable communities. Traditional resilience analysis has been confined mostly to the use of static data collected at scheduled intervals. With the advent of the "Big Data" era, real-time human response data extracted from social media could provide new opportunities for studying disparities in disaster resilience. The investigators will compare Twitter data from two events, Hurricane Isaac and Hurricane Sandy, both of which occurred in 2012. They will pursue four interrelated research components: (1) development of data-mining algorithms for the evaluation and visualization of Twitter data; (2) analysis of social media and resilience disparities at the community (zip-code) level across the emergency management cycle; (3) an online survey of Twitter and non-Twitter users to understand effects of social media use on individual resilience, social networks, social support, and social disparities; and (4) analysis of bi-directional communication among residents and governmental and non-governmental organizations throughout each event cycle. Through an investigation of the Twitter data before, during, and after the hurricanes coupled with analysis of online surveys, the researchers will be able to address a set of core research questions: (1) Are there distinct geographical and social disparities in the use of social media in disaster resilience? (2) What are the sources and consequences of such disparities? (3) How do these disparities vary across the four phases of the disaster cycle? (4) How do these disparities affect resilience? (5) How can social media data be used to improve resilience? This project is supported through the NSF Interdisciplinary Behavioral and Social Sciences Research (IBSS) competition.

这个跨学科的研究项目将研究在应急管理的四个阶段（缓解，准备，响应和恢复）是否存在社会和地理差异。研究人员将使用多种视角和尺度来解决研究问题，包括在社区，个人和组织尺度上的分析。本项目的研究结果将为区域差异、个人社交网络和行为以及政府/组织政策之间的相互作用提供有价值的见解。该项目将扩大对社交媒体的使用是否有助于克服或进一步加深应急管理每个阶段的社会和地理差异的了解。该项目将提高对如何进行有效挖掘社交媒体数据的理解，以产生有用和有效的科学信息，从而通过开发和测试可用于从Twitter等来源挖掘噪声和不完善数据的算法来推进社会科学和信息科学研究。从该项目中获得的知识将有助于制定减少差距的战略，开展有效的社会媒体宣传活动和应急管理外联活动，并促进抗灾能力。本项目中使用的方法将适用于研究其他地区的其他灾害。了解灾害恢复能力的社会和地理差异的来源，模式和后果对于建立长期弹性，健康和可持续的社区至关重要。传统的复原力分析主要局限于使用按预定时间间隔收集的静态数据。随着“大数据”时代的到来，从社交媒体中提取的实时人类反应数据可以为研究抗灾能力的差异提供新的机会。调查人员将比较两个事件的推特数据，飓风艾萨克和飓风桑迪，这两个事件都发生在2012年。他们将追求四个相互关联的研究组成部分：（1）开发数据挖掘算法，用于评估和可视化Twitter数据;（2）分析社区的社交媒体和弹性差异（邮编）级应急管理全周期;（3）对Twitter和非Twitter用户进行在线调查，以了解社交媒体使用对个人韧性、社交网络、社会支持和社会差距;（4）分析居民与政府和非政府组织在每个活动周期中的双向沟通。通过对飓风之前、期间和之后的Twitter数据的调查，以及对在线调查的分析，研究人员将能够解决一系列核心研究问题：（1）在使用社交媒体进行抗灾方面是否存在明显的地理和社会差异？ (2)这种差异的根源和后果是什么？ (3)这些差异在灾害周期的四个阶段中有何不同？ (4)这些差异如何影响复原力？ (5)如何利用社交媒体数据来提高弹性？该项目通过NSF跨学科行为和社会科学研究（IBSS）竞赛获得支持。

项目成果

期刊论文数量（12）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Use of Twitter in disaster rescue: lessons learned from Hurricane Harvey

在灾难救援中使用 Twitter：从飓风哈维中吸取的教训

DOI：
10.1080/17538947.2020.1729879
发表时间：
2020
期刊：
International Journal of Digital Earth
影响因子：
5.1
作者：
Mihunov, Volodymyr V.;Lam, Nina S.;Zou, Lei;Wang, Zheye;Wang, Kejin
通讯作者：
Wang, Kejin

Towards Distributed Cyberinfrastructure for Smart Cities Using Big Data and Deep Learning Technologies

利用大数据和深度学习技术构建智慧城市的分布式网络基础设施

DOI：
10.1109/icdcs.2018.00127
发表时间：
2018
期刊：
2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS
影响因子：
0
作者：
Shams, Shayan;Goswami, Sayan;Lee, Kisung;Yang, Seungwon;Park, Seung-Jong
通讯作者：
Park, Seung-Jong

Extending Getis–Ord Statistics to Account for Local Space–Time Autocorrelation in Spatial Panel Data