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

CHS：小型：协作研究：自动化社交媒体数据中的相关性和信任检测以进行紧急响应

• 批准号: 1903963
• 负责人: Cornelia Caragea
• 金额: $ 22.97万
• 依托单位: University of Illinois at Chicago
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-26 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1903963&HistoricalAwards=false
• 关键词: CHS; Small; Collaborative; Research; Automating

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.

该项目的目标是开发提高信息质量和在应急响应中使用的方法，提高在灾难期间使用来自非专业参与者群体的消息和微博数据的价值。尽管有证据表明对于那些经历灾难的人和那些寻求有关灾难的信息的人来说具有很大的价值，但在检测社交媒体流中消息的相关性和准确性方面却很少做出努力。 数据验证问题是紧急响应组织在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信息的方法直接解决这个已知问题。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紧急情况的组织利用大量公民产生的数据，从而提高应急响应的速度、质量和效率，从而为有需要的人提供更好的支持，并挽救更多的生命。这项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，为应急和灾害研究领域做出贡献以及每个的可验证标准。它还将调查相关且可验证的标识符（或特征），提供权重，将其合并到分析框架中，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，可以估计由短消息组成的大量流文本中消息的相关性和准确性。鉴于团队背景的多元化，将有助于社会技术系统理论的运用和发展来分析技术与社会系统的整合。模型的输出将符合响应组织的组织需求。