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EAGER: Collaborative Research: Establishing Trustworthy-Citizen-Created Data for Disaster Response and Humanitarian Action

EAGER: Collaborative Research: Establishing Trustworthy-Citizen-Created Data for Disaster Response and Humanitarian Action
EAGER:协作研究:为灾难响应和人道主义行动建立值得信赖的公民创建的数据
批准号:
1353400
负责人:
Andrea Tapia
金额:
$7.0万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2013
资助国家:
美国
项目状态:
已结题
起止时间:
2013-09-01 至 2015-08-31

项目摘要

项目成果

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中文摘要
翻译
通常被称为微博,普通公民在灾害期间报告“实地”活动的做法越来越普遍。这些消息的内容对于响应者组织和受害者来说可能是有价值的,但是它们的数量使得很难从流中分离出有价值的消息。该项目将审查灾害期间发送的微博消息,以确定这些消息的哪些方面(单独和集体)表明它们是相关的、可核查的和可采取行动的。要考虑的因素包括信息的内容、发送者的身份以及信息的总体模式和传播。然后,识别出的因素将用于指导将标记消息的众包工作人员创建一个大型的标记消息语料库。该项目之所以重要,是因为微博客数据被视为越来越重要:它们无处不在,快速和可访问,并被认为使普通公民能够在灾害期间更加了解情况,并协调自助。如果该项目取得成功,其结果将证明有可能从微博信息流中识别相关、可核实和可采取行动的信息,并确定证据因素。进一步的成果将是一个与灾害相关的、有标签的信息数据集,这对研究人员很有用,例如,那些寻求自动分类微博数据流中的信息的人。
英文摘要
Often referred to as microblogging, the practice of average citizens reporting on activities "on-the-ground" during a disaster is increasingly common. The contents of these message are potentially valuable to responder organizations and victims, but their volume makes it difficult to separate valuable messages from the stream. This project will examine microblogged messages sent during disasters to determine what aspects of the messages (individually and collectively) indicate that they are relevant, verifiable and actionable. Factors to be considered include the content of the messages, the identity of the sender and the overall pattern and spread of messages. The identified factors will then be used to instruct crowdsourced workers who will label messages to create a large corpus of labelled messages. The project is important because microblogging data are seen as increasingly important: they are ubiquitous, rapid and accessible, and they are believed to empower average citizens to become more situationally aware during disasters and to coordinate to help themselves. The result of the project, if it is successful, will be evidence that it is possible to identify relevant, verifiable and actionable messages from a stream of microblogged messages and identification of the evidentiary factors. A further outcome will be a disaster-related, labeled dataset of messages, which will be useful to researchers, e.g., those seeking to automatically classify information within a microblogged data stream.
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BIGDATA: IA: Collaborative Research: Domain Adaptation Approaches for Classifying Crisis Related Data on Social Media
CHS: Small: Collaborative Research: Automating Relevance and Trust Detection in Social Media Data for Emergency Response
CRISP Type 2/Collaborative Research: Resilience Analytics: A Data-Driven Approach for Enhanced Interdependent Network Resilience
VOSS: HRCT Scanning as Glue: Sociotechnical Analysis and Support of a Loosely-Coupled Virtual Organization of Emergent Distributed Projects
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