RAPID: The Changing Roles of Social Media in Disaster Resilience: The Case of Hurricane Harvey

RAPID：社交媒体在抗灾能力中不断变化的角色：以飓风哈维为例

• 批准号: 1762600
• 负责人: Nina Lam
• 金额: $ 20万
• 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: 2018
• 资助国家: 美国
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1762600&HistoricalAwards=false
• 关键词: RAPID; Changing; Roles; Social; Media

Understanding the changing roles and effects of social media use in disaster events, such as Hurricane Harvey in Texas, will help reduce vulnerability and improve resilience of communities to these disaster events. Hurricane Harvey made landfall on August 25, 2017 near Rockport, Texas as a category-4 hurricane. It lingered over the Houston area and dumped over 50-inches of rainfall, causing widespread flooding and damages in the region. This unprecedented disastrous event reveals many issues, including inadequate flood warning and slow response by agencies. At the same time, a new phenomenon emerged during the Harvey event: many residents in the Houston area resorted to social media to call for rescue from flooded homes when the 911 system was overloaded and could not be connected. This changing use of social media marks Harvey as one of the very first disastrous events in which social media have played an important role in facilitating fast-responding rescue missions. The overarching research question is: how effective is social media in enhancing resilience through its new role in response and rescue, and do we see an increase or decrease in the geographical and social disparities of social media use that may have affected the outcome and the resilience of individuals and communities? This project collects time-sensitive Twitter data and online surveys of individuals and organizations in the flood-affected communities in the Houston region so that they can be used to address this key question. The research team collects five types of data including (1) Twitter data during the Harvey event; (2) associated webpages and multimedia embedded in tweets; (3) two time-series online surveys to track residents' sentiment, adaptation strategies, and decisions under uncertainty to stay or leave; (4) online surveys of agencies and residents regarding the new use of Twitter in rescue operations; (5) geographic information layers such as flood maps, damage, and socioeconomic data used to integrate with the others types of data. Methods for mining Twitter data and social network analysis are tested. These first-hand, timely collected data provide information on key concerns, sentiment, and adaptive behavior of individuals and organizations. This is valuable information for decision makers and first responders, thereby supporting efforts to map out better strategies to reduce vulnerability and improve resilience. Results from this project can also be compared with the hurricane events in 2012, thus gaining further insights into whether disparities have increased or decreased and where. From a computational point of view, developing better and more efficient algorithms for mining big data will advance the computation and analysis of resilience. The websites, databases, and reports derived from this project will be available and widely disseminated. The lessons learnt and the methodology used in this project can be utilized to study and compare different disasters in different regions.

了解社交媒体在德克萨斯州飓风中使用社交媒体在灾难事件中的作用和影响的变化将有助于减少脆弱性并提高社区对这些灾难事件的韧性。哈维飓风于2017年8月25日在德克萨斯州罗克波特附近登陆，为4类飓风。它在休斯顿地区徘徊，倾倒了超过50英寸的降雨，在该地区造成了广泛的洪水和损失。这一空前的灾难性事件揭示了许多问题，包括洪水警告不足和机构的缓慢反应。同时，在哈维活动期间出现了一种新现象：休斯顿地区的许多居民诉诸社交媒体，要求当911系统被超载且无法连接时，要求从被洪水淹没的房屋中营救。这种不断变化的社交媒体的使用标志着哈维是社交媒体在促进快速响应救援任务中发挥重要作用的第一个灾难性事件之一。总体研究问题是：通过其在响应和救援中的新作用来增强弹性的有效性，我们是否看到社交媒体使用的地理和社会差异的增加或减少可能影响了结果和弹性个人和社区？该项目收集了时间敏感的Twitter数据和在线调查，对休斯顿地区受洪水影响的社区的个人和组织进行了在线调查，因此可以使用它们来解决这个关键问题。研究团队在Harvey活动期间收集了五种类型的数据，包括（1）Twitter数据； （2）嵌入在推文中的相关网页和多媒体； （3）两个时间序列的在线调查，以跟踪居民的情感，适应策略和不确定性的决定； （4）在线调查机构和居民有关Twitter在救援行动中的新使用； （5）用于与其他类型的数据集成的地理信息层，例如洪水图，损害和社会经济数据。测试了针对Twitter数据和社交网络分析的方法。这些第一手，及时收集的数据提供了有关个人和组织的关键问题，情感和适应性行为的信息。这是对决策者和急救人员的宝贵信息，因此支持努力制定更好的策略，以减少脆弱性和提高弹性。该项目的结果也可以与2012年的飓风事件进行比较，从而进一步了解差异是否增加或减少以及在哪里。从计算的角度来看，开发更好，更有效的算法以挖掘大数据将提高弹性的计算和分析。该项目得出的网站，数据库和报告将提供并广泛传播。该项目中所学的经验教训和方法可以用于研究和比较不同地区的不同灾害。

项目成果

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
• 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
• 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
• 作者: Shams, Shayan;Platania, Richard;Park, Seung-Jong
• 通讯作者: Park, Seung-Jong