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.

了解社交媒体在灾难事件（如德克萨斯州的哈维飓风）中不断变化的角色和影响，将有助于减少脆弱性，提高社区对这些灾难事件的应变能力。飓风哈维于2017年8月25日在德克萨斯州罗克波特附近登陆，为四级飓风。它在休斯顿地区徘徊，带来了超过50英寸的降雨，在该地区造成了广泛的洪水和破坏。这场史无前例的灾难性事件暴露了许多问题，包括洪水预警不足和各机构反应迟缓。与此同时，在哈维飓风期间出现了一个新现象：当911系统过载无法连接时，休斯顿地区的许多居民在被洪水淹没的房屋中求助于社交媒体。这种对社交媒体使用方式的改变，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首批灾难性事件之一。首要的研究问题是：社交媒体通过其在应对和救援中的新作用，在增强恢复力方面有多有效？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，这些差异可能会影响结果和个人和社区的恢复力？这个项目收集了时间敏感的Twitter数据，以及休斯顿地区受洪水影响社区的个人和组织的在线调查，以便他们可以用来解决这个关键问题。研究团队收集了五种类型的数据，包括(1)哈维事件期间的Twitter数据；(2)推文中嵌入的关联网页和多媒体；(3)两项时间序列在线调查，追踪不确定性下居民的情绪、适应策略和留下或离开的决策；(4)对机构和居民就Twitter在救援行动中的新应用进行在线调查；(5)地理信息层，如洪水图、灾情和社会经济数据，用于与其他类型的数据集成。测试了挖掘Twitter数据和社交网络分析的方法。这些第一手的、及时收集的数据提供了关于个人和组织的关键关注点、情绪和适应性行为的信息。这对决策者和第一反应者来说是有价值的信息，从而支持制定更好的战略以减少脆弱性和提高复原力。该项目的结果还可以与2012年的飓风事件进行比较，从而进一步了解差异是增加了还是减少了，以及在哪里。从计算的角度来看，开发更好、更高效的大数据挖掘算法将推进弹性的计算和分析。这个项目产生的网站、数据库和报告将提供并广泛传播。本项目所吸取的经验教训和采用的方法可用于研究和比较不同地区的不同灾害。

项目成果

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
• 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

• 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

A hybrid and scalable error correction algorithm for indel and substitution errors of long reads

• DOI: 10.1186/s12864-019-6286-9
• 发表时间: 2019-12-20
• 期刊: BMC GENOMICS
• 影响因子: 4.4
• 作者: Das, Arghya Kusum;Goswami, Sayan;Park, Seung-Jong
• 通讯作者: Park, Seung-Jong