Forced Migration


Effective early warning of forced population displacement will help governments and international organizations plan for such movements, as well as directly aid potential refugees and displaced persons before, during and after their exodus. Planning can lead to action in trying to avert mass displacement by tackling the triggering events and underlying stressors, and providing options to those who would otherwise be forced to relocate (e.g., deploying peacekeepers high-risk communities in conflict zones or getting food to villages at risk of famine). Earlier warning may also help divert forced migrants from risky modes of movement (e.g., via non-seaworthy boats or across landmine infested borders). Early warning of displacement would enable governments and international organizations to pre-position shelter, food, medicines and other supplies in areas that are likely to receive large numbers of refugees and displaced persons. It will also help them prepare for what are often now unexpected return movements of refugees and displaced persons to their home countries and communities.

With funding from the National Science Foundation, Georgetown University has assembled a multidisciplinary community of scholars and practitioners to create a pilot of a large-scale, data intensive early warning system for detecting forced population displacement. The system is based on the Expandable Open Source (EOS) database (formerly known as Raptor), a vast unstructured archive at Georgetown University of over 600 million publicly available open-source media articles, and supplemented by social media such as Twitter. In addition to the open-source and social media data, our system has incorporated data from fieldwork (supported by previous funding mechanisms) with Iraqi refugees, whose recounts of personal experiences regarding forced displacement enriched our dataset and research methods. Mobilizing vast amounts of open source data will enable discovery of patterns of acute events (triggers) and/or slow-onset processes (trends) in the context of pre-existing stressors. It also supports simulation models that will help planners test the efficacy of different responses based on scenarios consistent with the early warning information produced.

Developing an effective early warning system of population displacement requires collaboration and shared learning between subject matter experts who understand the factors that contribute to forced migration and technical experts who understand how to collect, store, mine and analyze masses of data derived from international, national and local sources. Our Project team members have included scholars renowned in their respective fields, from Georgetown (US), Fairfield (US), Fordham (US), York (Canada), University of Toronto (Canada), Sussex (UK) and Kultur (Turkey) Universities, Lawrence Livermore National Laboratory, and practitioners from the Jesuit Relief Services, Refugees International, Women’s Refugee Commission and the Brookings-LSE Project on Internal Displacement. Bringing together social scientists and computer scientists during this planning period has exposed social scientists to new modeling approaches for analyzing their subject matter. At the same time, computer scientists have benefited from domain expertise in the social sciences, enhancing the intellectual merit of our project. This expertise has provided insight for the development of beyond state of the art data mining and machine learning of very large, incomplete and potentially biased open source databases for topic modeling, event detection, sequential mining, change detection, sentiment analysis and dynamic graph mining.

The Team

Principal Investigators:

  • Susan Martin, Institute for the Study of International Migration
  • Jeffrey Collmann, Microbiology and Immunology Department
  • Lisa Singh, Computer Science Department
  • Sidney Berkowitz, Computer Science Department.

Project Team Members:

  • Georgetown University
  • Fairfield University
  • Istanbul Kültur University
  • University of Toronto
  • York University
  • Sussex University
  • Jesuit Refugee Services
  • Refugees International
  • Women’s Refugee Commission
  • Brookings-LSE Project on Internal Displacement


The outcomes of this endeavor have been:

  1. a community of international and regional partners of practitioners and researchers collaborating to improve early warning of forced population movements through human-computer analysis;
  2. methods and algorithms for using massive data that can serve as a blueprint for integrating computational models into new avenues of social science research;
  3. a plan for implementing our approach to reach new audiences and broaden the social impact of our project;
  4. knowledge transfer to a broader community through presentations of papers and model simulations at international conferences;
  5. pilot studies on forced displacement in and out of Syria, Iraq and Somalia;
  6. an experimental project on human trafficking in Central America; and,
  7. presentations and publications aimed at social and computer scientists as well as practitioners and policy makers in the forced migration field.


This material is based upon work supported by the National Science Foundation under Grant No. 1338507. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.