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.

该跨学科研究项目将检查在紧急管理的四个短语（缓解，准备，响应，响应和恢复）中是否存在社会和地理差异。调查人员将使用多种观点和量表来解决研究问题，包括在社区，个人和组织规模的分析。该项目的发现将为区域差异，个别社交网络和行为以及政府/组织政策之间的相互作用提供宝贵的见解。该项目将扩大有关社交媒体使用是否可以克服或进一步加深紧急管理阶段的社会和地理差异的知识。该项目将增强对如何进行社交媒体数据有效采矿以产生有用和有效的科学信息的理解，从而通过开发和测试可以用来挖掘可从中挖掘出嘈杂和不完美数据的社会科学和信息科学研究的了解像Twitter这样的消息来源。从该项目中获得的知识将有助于制定策略，以减少差异，创建有效的社交媒体运动和紧急管理宣传，并促进对灾难的韧性。该项目中使用的方法将适用于研究其他地区的其他灾难。理解灾难恢复中社会和地理差异的来源，模式和后果对于建立长期韧性，健康和可持续的社区至关重要。传统的弹性分析主要仅限于按计划间隔收集的静态数据。随着“大数据”时代的出现，从社交媒体中提取的实时人类响应数据可以为研究灾难恢复能力的差异提供新的机会。研究人员将比较来自两个事件的Twitter数据，即ISAAC飓风和飓风Sandy，两者均在2012年发生。他们将追求四个相互关联的研究组件：（1）开发数据挖掘算法以评估和可视化Twitter数据；（2）在紧急管理周期内对社区（ZIP-CODE）水平的社交媒体和弹性差异分析；（3）在线调查Twitter和非twitter用户，以了解社交媒体使用对个人韧性，社交网络，社交支持和社会差异的影响；（4）在每个事件周期中，居民以及政府和非政府组织之间的双向交流分析。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）在使用方面存在明显的地理和社会差异。社交媒体在灾难弹性中？（2）这种差异的来源和后果是什么？（3）这些差异在灾难周期的四个阶段如何变化？（4）这些差异如何影响弹性？（5）如何使用社交媒体数据来提高弹性？ NSF跨学科的行为和社会科学研究（IBSS）竞赛得到了支持。

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）

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

DOI：
10.1080/00330124.2019.1709215
发表时间：
2020-02
期刊：
The Professional Geographer
影响因子：
0
作者：
Zheye Wang;N. Lam
通讯作者：
Zheye Wang;N. Lam

GraphMap: scalable iterative graph processing using NoSQL

GraphMap：使用 NoSQL 的可扩展迭代图形处理

DOI：
10.1007/s11227-019-03097-w
发表时间：
2019
期刊：
The Journal of Supercomputing
影响因子：
0
作者：
Goswami, Sayan;Pokhrel, Ayam;Lee, Kisung;Liu, Ling;Zhang, Qi;Zhou, Yang
通讯作者：
Zhou, Yang

Social and geographical disparities in Twitter use during Hurricane Harvey

DOI：
10.1080/17538947.2018.1545878
发表时间：
2019-01-01
期刊：
International Journal of Digital Earth
影响因子：
5.1
作者：
Lei Zou;Lam, Nina S. N.;Reams, Margaret A.
通讯作者：
Reams, Margaret A.

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

Deep Generative Breast Cancer Screening and Diagnosis

DOI：
10.1007/978-3-030-00934-2_95
发表时间：
2018-01-01
期刊：
MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2018, PT II
影响因子：
0
作者：
Shams, Shayan;Platania, Richard;Park, Seung-Jong
通讯作者：
Park, Seung-Jong