RAPID: The Changing Nature of "Calls" for Help with Hurricane Harvey: Comparing 9-1-1 and Social Media

RAPID：飓风“哈维”求助性质的变化：比较 9-1-1 和社交媒体

基本信息

批准号：
1760453
负责人：
Keri Stephens
金额：
$ 16.85万
依托单位：
University of Texas at Austin
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-10-01 至 2019-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1760453&HistoricalAwards=false
关键词：
RAPID Changing Nature Calls Help

项目摘要

RAPID: The Changing Nature of "Calls" for Help with Hurricane Harvey: 9-1-1 and Social MediaHurricane Harvey is the first big-data disaster where social media "calls" for help appear to have supplanted the overloaded 9-1-1 call systems; social media provided a visible, dialogic link to help. But this form of help-seeking behavior on public social media is relatively new. This project (1) captures the voices of hurricane victims and emergency response workers (both governmental and volunteer) (2) uses captured data to characterize the language present in actual social media calls for help, and (3) applies a big-data approach to a new emergency situation to assess that situation's calls for help. This project paves the way for new ways of thinking about how first-responders can utilize social media alongside traditional 9-1-1 when dispatching in future emergencies. The current practice in the crisis informatics literature is to mine social-media data during the disaster/aftermath around disaster-related keywords. However, such data collection pulls in everything--from solicitations for donations, to news stories--and it is challenging to filter signal from noise in such broad data sets. It is important to identify common threads in the language disaster victims use in their public "calls for help" to allow emergency managers to rapidly pinpoint these needs across varied communication channels and save lives. The approach in this project is unique because the combinatorial method isolates the signal of conversations by disaster victims on social media by understanding the specific keywords disaster victims use when requesting help. Using field interviews and surveys with Harvey and Irma victims, emergency response organizations, and organizations like the Texas/Cajun Navy--volunteer groups who organized their efforts through social media--the project will characterize what was posted, where calls for help were posted, and how these requests generated responses. The interview protocol will elicit examples of interviewees' social media posts to help develop ontologies of this content. In combination with historical data across several platforms (YouTube, Twitter, Reddit and Facebook) that will be purchased, the second phase of this project will match precise search queries (narrowed using boolean operators). The search mechanism will be driven by victims' social media behaviors and language specific to their experience of Harvey and Irma, rather than catchall hashtags and search terms. These types of victim-driven ontologies developed around specific experiences of a disaster are seriously lacking and understudied.

RAPID：飓风哈维“呼救”性质的变化：9-1-1 和社交媒体飓风哈维是第一场大数据灾难，社交媒体“呼救”似乎已经取代了超载的 9-1-1呼叫系统；社交媒体提供了可见的对话链接来提供帮助。但这种在公共社交媒体上寻求帮助的行为形式相对较新。该项目 (1) 捕捉飓风受害者和应急响应人员（政府和志愿者）的声音 (2) 使用捕捉到的数据来描述实际社交媒体求助中的语言特征，以及 (3) 应用大数据方法针对新的紧急情况来评估该情况的求助情况。该项目为急救人员在未来紧急情况下进行调度时如何利用社交媒体以及传统的 9-1-1 的新思维方式铺平了道路。危机信息学文献中当前的做法是围绕灾难相关关键词挖掘灾难/灾后的社交媒体数据。然而，这样的数据收集涵盖了从募捐到新闻报道的所有内容，并且在如此广泛的数据集中过滤掉噪音中的信号具有挑战性。重要的是要确定灾难受害者在公开“求助”中使用的语言的共同点，以便应急管理人员能够通过各种沟通渠道快速确定这些需求并拯救生命。该项目的方法是独特的，因为组合方法通过了解灾民在请求帮助时使用的特定关键字来隔离灾民在社交媒体上的对话信号。通过对哈维和艾尔玛受害者、应急响应组织以及德克萨斯州/卡真海军等组织（通过社交媒体组织努力的志愿者团体）进行现场采访和调查，该项目将描述所发布的内容以及发布的求助电话，以及这些请求如何生成响应。采访协议将引出受访者社交媒体帖子的示例，以帮助开发该内容的本体。结合将要购买的多个平台（YouTube、Twitter、Reddit 和 Facebook）的历史数据，该项目的第二阶段将匹配精确的搜索查询（使用布尔运算符缩小范围）。搜索机制将由受害者的社交媒体行为和针对哈维和艾尔玛经历的特定语言驱动，而不是笼统的标签和搜索词。这些围绕灾难的具体经历开发的受害者驱动的本体论严重缺乏且研究不足。

项目成果

期刊论文数量（10）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Citizens Communicating Health Information: Urging Others in their Community to Seek Help During a Flood

公民传播健康信息：敦促社区中的其他人在洪水期间寻求帮助

DOI：
发表时间：
2018
期刊：
Proceedings of the ... International ISCRAM Conference
影响因子：
0
作者：
Stephens, K. K.;Li, J.;Robertson, B. W.;Smith, W. R.
通讯作者：
Smith, W. R.

Jumping in and Out of the Dirty Water… Learning from Stories while Doing Social Science

在脏水里跳进跳出……在做社会科学的同时从故事中学习

DOI：
10.1080/10410236.2019.1580995
发表时间：
2019
期刊：
Health Communication
影响因子：
3.9
作者：
Stephens, Keri K.
通讯作者：
Stephens, Keri K.

Evaluating the performance of Deep learning methods for hurricane-related image classification.

评估飓风相关图像分类的深度学习方法的性能。

DOI：
发表时间：
2019
期刊：
Proceedings of the ... International ISCRAM Conference
影响因子：
0
作者：
Johnson, M.;Murthy, D.;Robertson, B. W.;Smith, W. R.;& Stephens, K. K.
通讯作者：
& Stephens, K. K.

Assessing the Stability of Tweet Corpora for Hurricane Events Over Time: A Mixed Methods Approach

DOI：
10.1145/3328529.3328545
发表时间：
2019-07
期刊：
Proceedings of the 10th International Conference on Social Media and Society
影响因子：
0
作者：
Estella Z. Xin;D. Murthy;Nandhini Lakuduva;K. Stephens
通讯作者：
Estella Z. Xin;D. Murthy;Nandhini Lakuduva;K. Stephens

Using social media to call for help in Hurricane Harvey: Bonding emotion, culture, and community relationships