National press monitoring using Natural Language Processing as an early warning signal for prediction of asylum applications flows in Europe.

Europe, during the last decade, is facing major migration flows, due to several crises out the border of its territory. In this regard, the prediction of migration flows place a significant r ole. The development of the internet and the rise of Big Data pave the path for the development of early warning systems with promising forecasting power. This paper presents a novel approach to migration prediction, using neural network architectures, in combination with Natural Language Processing techniques, that monitor and search for early signals in the national press of countries of origin and destination. This is the first study to use topic modeling to monitor the national press to predict migration. The pipeline proposed in this paper uses the following three major datasets: EUROSTAT, GDELT and ALL-NEWS. The topic classifier, which is proposed, consists of a Latent Dirichlet Allocation(LDA) model trained using the ALL-NEWS dataset. Big Data in the form of national press articles can be of tremendous value to the problem of migration prediction.