FLARE: federated active learning assisted by naming for responding to emergencies
FLARE: federated active learning assisted by naming for responding to emergencies
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DOI：
10.1145/3460417.3482978
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发表时间：
2021-09
期刊：
影响因子：
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通讯作者：
Viyom Mittal;Mohammad Jahanian;K. Ramakrishnan
中科院分区：
文献类型：
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作者：
Viyom Mittal;Mohammad Jahanian;K. Ramakrishnan
在灾难期间，向适当的第一反应人员提供紧急信息至关重要。基于名称的信息交付为分配了不同事件响应角色的第一响应团队提供了高效、及时的相关内容传播。人们越来越依赖社交媒体，使用自由格式的文本来交流重要信息。因此，一种将这些社交媒体帖子传递给正确的第一响应者的方法可以显著改善结果。在本文中，我们提出了FLARE，这是一个使用社交媒体引擎(SME)将社交媒体帖子(SMP)映射到正确名称的框架。中小企业以在线实时方式执行基于自然语言处理的分类并利用几种机器学习能力。为了减少灾难期间学习所需的手动标记工作，我们利用主动学习，并辅之以具有特定领域知识的调度员执行有限的标记。我们还利用具有专业知识的各个公共安全部门之间的联合学习，以合作的方式处理与其角色相关的通知。我们实现了三种不同的分类器：事件相关性、组织和细粒度角色预测。每个类与名称空间图的特定子集相关联。我们系统的创新之处在于将命名空间与联合主动学习和推理过程集成在一起，以便在分布式多组织环境中实时识别重要的SMP并将其提供给正确的急救人员。我们使用真实世界的数据进行的实验，包括2018年加州野火期间公民生成的推文，表明我们的方法比基于简单关键字的分类和几种现有的基于NLP的分类技术都要好。
During disasters, it is critical to deliver emergency information to appropriate first responders. Name-based information delivery provides efficient, timely dissemination of relevant content to first responder teams assigned to different incident response roles. People increasingly depend on social media for communicating vital information, using free-form text. Thus, a method that delivers these social media posts to the right first responders can significantly improve outcomes. In this paper, we propose FLARE, a framework using 'Social Media Engines' (SMEs) to map social media posts (SMPs), such as tweets, to the right names. SMEs perform natural language processing-based classification and exploit several machine learning capabilities, in an online real-time manner. To reduce the manual labeling effort required for learning during the disaster, we leverage active learning, complemented by dispatchers with specific domain-knowledge performing limited labeling. We also leverage federated learning across various public-safety departments with specialized knowledge to handle notifications related to their roles in a cooperative manner. We implement three different classifiers: for incident relevance, organization, and fine-grained role prediction. Each class is associated with a specific subset of the namespace graph. The novelty of our system is the integration of the namespace with federated active learning and inference procedures to identify and deliver vital SMPs to the right first responders in a distributed multi-organization environment, in real-time. Our experiments using real-world data, including tweets generated by citizens during the wildfires in California in 2018, show our approach outperforming both a simple keyword-based classification and several existing NLP-based classification techniques.