Predicting Forced Population Displacement Using News Articles


Sadra Abrishamkar and Forouq Khonsari, York University, Canada


The world has witnessed mass forced population displacement across the globe. Pop- ulation displacement has various indications, with different social and policy consequences. Mitigation of the humanitarian crisis requires tracking and predicting the population movements to allocate the necessary resources and inform the policymakers. The set of events that triggers pop-ulation movements can be traced in the news articles. In this paper, we propose the Population Displacement-Signal Extraction Framework (PD-SEF) to explore a large news corpus and extract the signals of forced population displacement. PD-SEF measures and evaluates violence signals, which is a critical factor of forced displacement from it. Following signal extraction, we propose a displacement prediction model based on extracted violence scores. Experimental results indicate the effectiveness of our framework in extracting high quality violence scores and building accurate prediction models.


Topic Modeling, Classification, Humanitarian Signal Extraction