Combining machine learning with social network analysis (SNA) can leverage vast amounts of social media data to better respond to crises. We present a case study using Twitter data from the March 2019 Nebraska floods in the United States, which caused over $1 billion in damage in the state and widespread evacuations of residents. We use a subset of machine learning, deep learning (DL), to classify text content of 11,982 tweets, and we integrate that with SNA to understand the structure of tweet interactions. Our DL approach pre‐trains our model with a DL language technique, BERT, and then trains the model using the standard training dataset to sort a dataset of tweets into classes tailored to crisis events. Several performance measures demonstrate that our two‐tiered trained model improves domain adaptation and generalization across different extreme weather event types. This approach identifies the role of Twitter during the damage containment stage of the flood. Our SNA identifies accounts that function as primary sources of information on Twitter. Together, these two approaches help crisis managers filter large volumes of data and overcome challenges faced by simple statistical models and other computational techniques to provide useful information during crises like flooding.

中文翻译：

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使用深度学习和社交网络分析来了解和管理极端洪水

将机器学习与社交网络分析（SNA）相结合可以利用大量的社交媒体数据来更好地应对危机。我们使用Twitter的数据进行案例研究，该数据来自2019年3月美国内布拉斯加州的洪水，造成该州超过10亿美元的损失和居民的大量疏散。我们使用机器学习，深度学习（DL）的子集对11,982条推文的文本内容进行分类，并将其与SNA集成以了解推文交互的结构。我们的DL方法使用DL语言技术BERT对我们的模型进行预训练，然后使用标准训练数据集对模型进行训练，以将推文数据集归类为针对危机事件的类。多项性能指标表明，我们的两层训练模型提高了跨不同极端天气事件类型的域适应性和泛化性。这种方法确定了Twitter在洪水的破坏遏制阶段中的作用。我们的SNA可以识别充当Twitter主要信息来源的帐户。这两种方法结合在一起，可以帮助危机管理人员过滤大量数据，并克服简单统计模型和其他计算技术所面临的挑战，以在洪水等危机期间提供有用的信息。