A Machine Learning Approach for Detecting Rescue Requests from Social Media
A Machine Learning Approach for Detecting Rescue Requests from Social Media
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DOI：
10.3390/ijgi11110570
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发表时间：
2022-11
期刊：
影响因子：
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通讯作者：
Zheye Wang;N. Lam;Mingxuan Sun;Xiao Huang;Jin Shang;Lei Zou;Yue Wu;V. Mihunov
中科院分区：
文献类型：
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作者：
Zheye Wang;N. Lam;Mingxuan Sun;Xiao Huang;Jin Shang;Lei Zou;Yue Wu;V. Mihunov
2017 年的哈维飓风标志着一个重要的转变，许多灾难受害者使用社交媒体而不是超负荷的 911 系统寻求救援。本文提出了一种基于机器学习的检测器，用于检测与哈维相关的 Twitter 消息的救援请求，该检测器通过考虑邮政编码对训练样本的准备和不同机器学习模型的性能的潜在影响，与现有检测器区分开来。我们研究了邮政编码过滤的结果与最近的一项类似研究在为机器学习模型生成训练数据方面的结果有何不同。接下来，通过模拟不同比例的邮政编码标记的正样本，进行实验来测试邮政编码的存在如何影响机器学习模型的性能。研究结果表明：（1）除了 K 最近邻和朴素贝叶斯之外的所有机器学习分类器在检测来自社交媒体的救援请求方面都达到了最先进的性能； (2) 使用邮政编码过滤可以提高收集救援请求以训练机器学习模型的效率； (3) 机器学习模型能够更好地识别与邮政编码相关的救援请求。因此，我们鼓励每个寻求救援的受害者在社交媒体上发布消息时包含邮政编码。这项研究是对文献的有益补充，有助于急救人员更有效地营救灾难受害者。
Hurricane Harvey in 2017 marked an important transition where many disaster victims used social media rather than the overloaded 911 system to seek rescue. This article presents a machine-learning-based detector of rescue requests from Harvey-related Twitter messages, which differentiates itself from existing ones by accounting for the potential impacts of ZIP codes on both the preparation of training samples and the performance of different machine learning models. We investigate how the outcomes of our ZIP code filtering differ from those of a recent, comparable study in terms of generating training data for machine learning models. Following this, experiments are conducted to test how the existence of ZIP codes would affect the performance of machine learning models by simulating different percentages of ZIP-code-tagged positive samples. The findings show that (1) all machine learning classifiers except K-nearest neighbors and Naïve Bayes achieve state-of-the-art performance in detecting rescue requests from social media; (2) using ZIP code filtering could increase the effectiveness of gathering rescue requests for training machine learning models; (3) machine learning models are better able to identify rescue requests that are associated with ZIP codes. We thereby encourage every rescue-seeking victim to include ZIP codes when posting messages on social media. This study is a useful addition to the literature and can be helpful for first responders to rescue disaster victims more efficiently.