Forecasting the Break
Building Community and Capacity for Large-Scale Data-Intensive Research in Forced Migration Studies
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.
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
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 primary source for long-term data for our 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. In addition, we have begun integrating social media data from Twitter, to expand our use of big data. Mobilizing vast amounts of open source data enables the discovery of patterns of acute events (triggers) and/or slow-onset processes (trends) in the context of pre-existing stressors. Developing an effective early warning system of population displacement continues to require 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. Bringing together social scientists and computer scientists exposes social scientists to new modeling approaches for analyzing their subject matter. At the same time, computer scientists are exploiting domain expertise in the social sciences. This expertise has provided insight for the development of state-of-the art data mining of large open source databases for event detection, sequential mining and change detection.
The team has completed its planning grant project, and has begun efforts for implementation. Team members will present preliminary results from the planning grant at a conference in May 2015.
This project is being funded by the National Science Foundation*. For further information, contact Katharine Donato or Lisa Singh.
*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.
Reports & Publications
- Donato, Katharine M., Lisa Singh, Elizabeth Jacobs, Ali Arab and Douglas Post. 2022. “Migration, Misinformation and Venezuelan Displacement during the COVID-19 Pandemic.” Harvard Data Science Review 4(1). https://hdsr.mitpress.mit.edu/pub/deut7pug/release/1?readingCollection=dd2b4f47
- Donato, Katharine M., Lisa Singh, Ali Arab, Elizabeth Jacobs and Douglas Post. 2021. “Migration Misinformation in Spanish-Language Tweets During a Pandemic.” Migration Research Series No. 68, October. Geneva: International Organization for Migration. https://publications.iom.int/books/mrs-no-68-migration-misinformation-spanish-language-tweets-during-pandemic
- Singh, Lisa, Laila Wahedi, Yanchen Wang, Yifang Wei, Christo Kirov, Susan Martin, Katharine Donato, Yaguang Liu, and Kornraphop Kawintiranon. 2019. “Blending Noisy Social Media Signals with Traditional Movement Variables to Predict Forced Migration.” KDD’19: Proceeding of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (July): 1975-83 https://doi.org/10.1145/3292500.3330774.
- Identification of Extremism on Twitter
- Measuring the Potential for Mass Displacement in Menacing Contexts