Twitter data are a valuable source of information for rescue and helping activities in case of natural disasters and technical accidents. Several methods for disaster‐ and event‐related tweet filtering and classification are available to analyse social media streams. Rather than processing single tweets, taking into account space and time is likely to reveal even more insights regarding local event dynamics and impacts on population and environment. This study focuses on the design and evaluation of a generic workflow for Twitter data analysis that leverages that additional information to characterize crisis events more comprehensively. The workflow covers data acquisition, analysis and visualization, and aims at the provision of a multifaceted and detailed picture of events that happen in affected areas. This is approached by utilizing agile and flexible analysis methods providing different and complementary views on the data. Utilizing state‐of‐the‐art deep learning and clustering methods, we are interested in the question, whether our workflow is suitable to reconstruct and picture the course of events during major natural disasters from Twitter data. Experimental results obtained with a data set acquired during hurricane Florence in September 2018 demonstrate the effectiveness of the applied methods but also indicate further interesting research questions and directions.

中文翻译：

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灾难发生在哪里？基于Twitter数据对危机事件进行多方面表征的工作流程

Twitter数据是在自然灾害和技术事故中进行救援和帮助活动的宝贵信息来源。有几种与灾难和事件相关的推特过滤和分类的方法可用于分析社交媒体流。与处理单个推文相比，考虑空间和时间可能会揭示出更多有关本地事件动态以及对人口和环境影响的见解。这项研究的重点是用于Twitter数据分析的通用工作流的设计和评估，该工作流利用这些附加信息来更全面地描述危机事件。该工作流涵盖数据采集，分析和可视化，旨在提供受影响区域中发生的事件的多方面和详细的图片。这是通过利用灵活而灵活的分析方法来提供的，这些方法提供了关于数据的不同和互补的观点。利用最先进的深度学习和聚类方法，我们对以下问题感兴趣：我们的工作流程是否适合从Twitter数据重构和描绘重大自然灾害期间的事件过程。利用2018年9月佛罗伦萨飓风期间获得的数据集获得的实验结果证明了所应用方法的有效性，但也表明了进一步有趣的研究问题和方向。我们的工作流程是否适合从Twitter数据中重建和描绘重大自然灾害期间的事件过程。利用2018年9月佛罗伦萨飓风期间获得的数据集获得的实验结果证明了所应用方法的有效性，但也表明了进一步有趣的研究问题和方向。我们的工作流程是否适合从Twitter数据中重建和描绘重大自然灾害期间的事件过程。利用2018年9月佛罗伦萨飓风期间获得的数据集获得的实验结果证明了所应用方法的有效性，但也表明了进一步有趣的研究问题和方向。