Research Findings

See below for a list of our published results about forced migration, algorithms for extracting signals from unstructured data and our project methodology
  • Donato, K.M., Singh, L., Arab, A., Jacobs, E., & Post, D. (2021). Migration misinformation in Spanish-language tweets during a pandemic. Migration Research Series N° 68. International Organization For Migration (IOM). Geneva. [Download]
  • Singh, L., Padden, C., Davis-Kean, P., David, R., Marwadi, V., Ren, Y., & Vanarsdall, R. (2021). Text Analytic Research Portals: Supporting Large-Scale Social Science Research. In IEEE International Conference on Big Data (pp. 6020-6022). [Download]
  • Donato, K. M., Singh, L., Arab, A., Jacobs, E., & Post, D. (2022). Misinformation About COVID-19 and Venezuelan Migration: Trends in Twitter Conversation During a Pandemic. Harvard Data Science Review. [Download]
  • Martin, S., Singh, L., Taylor, A., & Wahedi, L. (2021). Dynamic Model of Displacement. PsyArXiv. [Download]
  • Singh, L., Donato, K., Arab, A., Belon, T. A., Fraifeld, A., Fulmer, S., Post, D., & Wang, Y. (2020). Identifying Meaningful Indirect Indicators of Migration for Different Conflicts. KDD: ACM Humanitarian Workshop. [Download]
  • Singh, L., Wahedi, L., Wang, Y., Kirov, C., Wei, Y., Martin, S., Donato, K., Liu, Y., and Kawintiranon, K. (2019). Blending Noisy Social Media Signals with Traditional Movement Variables to Predict Forced Migration. ACM International Conference on Knowledge Discovery and Data Mining (KDD), Anchorage, Alaska. [Download]
  • Martin, S. and Singh, L.. (2018). Data analytics and displacement: Using big data to forecast mass movement of people. In Maitland, C., editor, Digital Lifeline?: ICTs for Refugees and Displaced Persons. MIT Press.
  • Hockett, J., Liu, Y., Wei, Y., Singh, L., Schneider, N. (2018) Detecting and Using Buzz from Newspapers to Understand Patterns of Movement. IEEE International Conference on Big Data (BIGDATA), Seattle, WA (Poster).
  • Wei Y., Singh L. (2018) Detecting Users Who Share Extremist Content on Twitter. In: Karampelas P., Bourlai T. (eds) Surveillance in Action. Advanced Sciences and Technologies for Security Applications. Springer, 351-368.
  • Wei, Y., Singh, L., Buttler, D., & Gallagher, B. (2018) Using Semantic Graphs to Detect Overlapping Target Events and Story Lines from Newspaper Articles. International Journal of Data Science and Analytics 5(1), 41-60.
  • Wei, Y. and Singh, L. (2017) Understanding the Impact of Sampling and Noise on Detecting Events using Twitter. IEEE International Conference on Big Data (BIGDATA), Boston, MA (Poster).
  • Singh, L. and Pemmaraju, R. (2017) EOS: A Multilingual Text Archive of International Newspaper & Blog Articles. IEEE International Conference on Big Data (BIGDATA), Boston, MA (Poster).
  • Wei, Y. and Singh, L. (2017) Location-based Event Detection Using Geotagged Semantic Graphs. Workshop on Mining and Learning with Graphs (MGL) at the ACM International Conference on Knowledge Discovery and Data Mining (KDD). Nova Scotia, CA.
  • Wei, Y. and Singh, L. (2017) Using Network Flows to Identify Users Sharing Extremist Content on Social Media. Pacific Asian Conference on Knowledge Discovery and Data Mining (PAKDD), Jeju, South Korea.
  • Wei, Y., Singh, L. and Martin, S. (2016) Identification of Extremism on Twitter. Workshop on Social Network Analysis Surveillance Technologies at the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), San Francisco, CA.
  • Wei, Y., Singh, L., Gallagher, B., & Butler, D. (2016) Overlapping Target Event and Storyline Detection of Online Newspaper Articles. IEEE International Conference on Data Science and Advanced Analytics. Montreal, Canada.
  • Singh, L. (2016). Data Ethics—Attaining Personal Privacy on the Web. In J. Collmann & S. A. Matei (Eds.), Ethical Reasoning in Big Data: An Exploratory Analysis (pp. 81–90). New York: Springer.
  • Collmann, J., Blake, J., Kinne, L., Dillon, R., Bridgeland, D., Martin, S., et al. (2016). Measuring the Potential for Mass Displacement in Menacing Contexts. Journal of Refugee Studies, 29, 273–294.
  • Wei, Y., Taylor, A., Yossinger, N. S., Swingewood, E., Cronbaugh, C., Quinn, D. R., et al. (2014). Using Large-scale Open Source Data to Identify Potential Forced Migration. KDD Workshop on Data Science for Social Good.
  • Martin, S., Weerasinghe, S., & Taylor, A. (2014). Migration and Humanitarian Crises: Causes, Consequences and Responses. New York, NY: Routledge.
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