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

快速：飓风哈维：9-1-1和社交媒体求救的性质哈维飓风是第一场大数据灾难，社交媒体的求救似乎取代了超载的9-1-1求救系统；社交媒体提供了一个可见的、对话式的求救链接。但这种在公共社交媒体上的求助行为相对较新。该项目(1)捕获飓风受害者和应急工作人员(包括政府和志愿者)的声音；(2)使用捕获的数据来描述实际社交媒体求助中存在的语言；以及(3)将大数据方法应用于新的紧急情况，以评估该情况的求助。这个项目为思考第一反应者在未来紧急情况下如何利用社交媒体以及传统的9-1-1进行调度的新方法铺平了道路。目前在危机信息学文献中的做法是围绕与灾难相关的关键字在灾难/善后期间挖掘社交媒体数据。然而，这样的数据收集囊括了一切--从募捐到新闻报道--在如此广泛的数据集中过滤信号和噪音是一项挑战。重要的是要确定灾民在他们的公共“求救”中使用的语言的共同主线，以使应急管理人员能够通过不同的沟通渠道迅速确定这些需求，并拯救生命。这个项目中的方法是独特的，因为组合方法通过理解灾难受害者在请求帮助时使用的特定关键字，隔离了灾难受害者在社交媒体上对话的信号。通过对哈维和伊尔玛受害者、应急组织以及像德克萨斯/卡津海军这样的组织--通过社交媒体组织他们的努力--的实地采访和调查，该项目将描述发布的内容、发布的求助信息以及这些请求如何产生响应。采访协议将引出受访者在社交媒体上发布的帖子的例子，以帮助开发这些内容的本体。结合将购买的几个平台(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语言驱动，这些行为和语言针对他们对哈维和伊尔玛的经历，而不是包罗万象的标签和搜索词。这些类型的受害者驱动的本体论是围绕灾难的具体经历发展起来的，严重缺乏，也没有得到充分的研究。

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.