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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
普通公民在灾难发生时报道“现场”活动的做法,通常被称为微博,正变得越来越普遍。这些消息的内容对响应组织和受害者具有潜在的价值,但是它们的数量使得很难将有价值的消息从流中分离出来。该项目将审查灾难期间发送的微博信息,以确定信息的哪些方面(单独和集体)表明它们是相关的、可核实的和可操作的。要考虑的因素包括消息的内容、发送者的身份以及消息的整体模式和传播。然后,确定的因素将用于指导将标记消息的众包工作人员创建大量标记消息的语料库。这个项目很重要,因为微博数据被视为越来越重要:它们无处不在、快速、可获取,而且它们被认为能让普通公民在灾难发生时更加了解情况,并协调自救。该项目的结果,如果成功,将证明有可能从微博消息流中识别相关的、可验证的和可操作的信息,并确定证据因素。进一步的结果将是一个与灾难相关的、标记的信息数据集,这将对研究人员有用,例如,那些寻求在微博数据流中自动分类信息的人。
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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