CHS: Small: Collaborative Research: Automating Relevance and Trust Detection in Social Media Data for Emergency Response

该项目的目标是开发手段，改进信息质量和在应急反应中的使用，增加在灾害期间使用来自大量非专业参与者的信息和微博数据的价值。尽管有证据表明，对于经历灾难的人和寻求有关灾难的信息的人来说，这些证据具有很强的价值，但在检测社交媒体流中消息的相关性和真实性方面，几乎没有做出什么努力。数据验证问题是应急组织考虑使用社交媒体数据时面临的最大问题之一。这项研究通过测量相关和可验证的信息的方法直接解决了这一已知问题。这项研究的结果将直接传递给参与应急响应的组织。因此，这项研究有可能帮助应对突发事件的组织利用大量公民产生的数据，这反过来可能会提高应急响应的速度、质量和效率，从而更好地支持需要它们的人，并拯救更多的生命。本研究将通过绘制应急响应期间做出的关键决策、这些决策点期间的信息需求、类型、形式和流程，以及最重要的是，评估数据质量和每个决策点的可验证标准，为应急和灾难研究领域做出贡献。它还将调查相关和可验证的标识(或特征)，提供权重，将这些纳入分析框架，并将分析结果用作可扩展计算模型的输入。这项工作将设计算法，可以在由短消息组成的大量流媒体文本中估计消息的相关性和准确性。鉴于该团队的不同背景，这将有助于社会技术系统理论的使用和发展，以分析技术和社会系统的整合。模型的输出将与响应组织的组织需求相匹配。

The goal of this project is to develop means to improve information quality and use in emergency response, increasing the value of using messaging and microblogged data from crowds of non-professional participants during disasters. Despite the evidence of strong value to those experiencing the disaster and those seeking information concerning the disaster, there has been very little effort in detecting the relevance and veracity of messages in social media streams. The problem of data verification is one of the largest problems confronting emergency-response organizations contemplating using social media data. This research directly addresses this known problem by methods to measure relevant and verifiable information. The results of this research will have a direct pipeline to organizations involved in emergency response. Therefore the research has the potential to help organizations, which respond to emergencies, make use of large amounts of citizen-produced data, which in turn may improve the speed, quality, and efficiency of emergency response leading to better support to those who need them, and more lives saved.This research will contribute to the field of Emergency and Disaster Studies by mapping the key decisions made during an emergency response, the information needs, type, form and flow during those decision points, and most importantly, assessing data quality and verifiable standards for each. It will also investigate relevant and verifiable identifiers (or features), provide weights, incorporate these into an analytical framework, and use the results of the analysis as input to scalable computational models. The work will design algorithms that can estimate the relevance and veracity of messages in a high-volume streaming text comprised of short messages. Given the diverse backgrounds of the team, it will contribute to the use and development of socio-technical systems theory to analyze the integration of technical and social systems. The output of the models will match the organizational needs of responding organizations.