In this period, the refugee movements led to challenging situations in the affected countries as the movements along the Balkan route did not result from a coordinated decision among these countries but instead developed gradually. From 2015 until March 2016, 1.2 million people moved along this route. One of the primary paths to Europe was the informal “Balkan route” that spans from Turkey to Central Europe through Greece, North Macedonia, Albania, Kosovo, Montenegro, Serbia, Bosnia and Herzegovina, Slovenia, and Hungary. Ongoing conflicts in the Near East led to mass refugee movements to Central Europe in 20 when more than 2.5 million people applied for asylum in member states of the European Union. We demonstrate that the approach proposed in this article benefits refugee management and vastly improves the status quo. 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. The results include spatial patterns and factual information about collective refugee movements extracted from social media data that match actual movement patterns. 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. Therefore, we propose an approach utilising machine learning methods and publicly available data to provide more information about refugee movements. 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.

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.

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