FUSED: Fusing Social Media Stream Classification Techniques for Effective Disaster Response
FUSED: Fusing Social Media Stream Classification Techniques for Effective Disaster Response
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DOI：
10.1109/cps-er56134.2022.00013
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发表时间：
2022-05
期刊：
影响因子：
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通讯作者：
Viyom Mittal;Hongmiao Yu;K. Ramakrishnan
中科院分区：
文献类型：
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作者：
Viyom Mittal;Hongmiao Yu;K. Ramakrishnan
及时将正确的信息传递给正确的第一响应者可以帮助改善他们努力的结果并挽救生命。随着社交媒体通信（Twitter， Facebook等）越来越多地用于在灾难期间发送和获取信息，及时将它们转发给正确的第一响应者会非常有帮助。我们使用自然语言处理和机器学习，将社交媒体帖子引导到最合适的第一响应者。一个重要的目标是检索并实时向第一响应者提供关键的、可操作的信息。我们检查了整个管道，从从社交媒体平台检索推文开始，到它们的分类，并传播到第一响应者。通过融合NLP和ML分类技术，我们提出了在数据检索、相关性预测和发送给第一响应者的信息优先级方面的改进，从而提高应急响应能力。我们在检索和提取与2021年8月至9月在美国发生的IDA飓风相关的37,295条可操作推文中证明了我们提出的方法的有效性。
Timely delivery of the right information to the right first responders can help improve the outcomes of their efforts and save lives. With social media communications (Twitter, Facebook, etc.) being increasingly used to send and get information during disasters, forwarding them to the right first responders in a timely manner can be very helpful. We use Natural Language Processing and Machine Learning, to steer the social media posts to the most appropriate first responder.An important goal is to retrieve and deliver only the critical, actionable information to first responders in real-time. We examine the overall pipeline starting from retrieving tweets from the social media platforms, to their classification, and dissemination to first responders.We propose improvements in the area of data retrieval, relevance prediction and prioritizing information sent to the first responders by fusing NLP and ML classification techniques thus improving emergency response. We demonstrate the effectiveness of our proposed approach in retrieving and extracting 37,295 actionable tweets related to the IDA hurricane that occurred in the US in Aug.–Sep, 2021.