In 2015, within the timespan of only a few months, more than a million people made their way from Turkey to Central Europe in the wake of the Syrian civil war. At the time, public authorities and relief organisations struggled with the admission, transfer, care, and accommodation of refugees due to the information gap about ongoing refugee movements. Therefore, we propose an approach utilising machine learning methods and publicly available data to provide more information about refugee movements. The approach combines methods to analyse the textual, temporal and spatial features of social media data and the number of arriving refugees of historical refugee movement statistics to provide relevant and up to date information about refugee movements and expected numbers. The results include spatial patterns and factual information about collective refugee movements extracted from social media data that match actual movement patterns. Furthermore, our approach enables us to forecast and simulate refugee movements to forecast an increase or decrease in the number of incoming refugees and to analyse potential future scenarios. We demonstrate that the approach proposed in this article benefits refugee management and vastly improves the status quo.

中文翻译：

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社交媒体数据和难民流动统计的时空机器学习分析

2015 年，在短短几个月的时间里，叙利亚内战后，超过 100 万人从土耳其前往中欧。当时，由于有关正在进行的难民流动的信息差距，公共当局和救援组织在难民的接纳、转移、照顾和住宿方面遇到了困难。因此，我们提出了一种利用机器学习方法和公开数据来提供有关难民流动的更多信息的方法。该方法结合分析社交媒体数据的文本、时间和空间特征以及历史难民流动统计数据的到达难民人数的方法，以提供有关难民流动和预期人数的最新相关信息。结果包括从与实际移动模式相匹配的社交媒体数据中提取的关于集体难民移动的空间模式和事实信息。此外，我们的方法使我们能够预测和模拟难民流动，以预测入境难民数量的增加或减少，并分析潜在的未来情景。我们证明本文中提出的方法有利于难民管理并极大地改善现状。