Blending Noisy Social Media Signals with Traditional Movement Variables to Predict Forced Migration

Abstract

Worldwide displacement due to war and conflict is at all-time high. Unfortunately, determining if, when, and where people will move is a complex problem. This paper proposes integrating both publicly available organic data from social media and newspapers with more traditional indicators of forced migration to determine when and where people will move. We combine movement and organic variables with spatial and temporal variation within different Bayesian models and show the viability of our method using a case study involving displacement in Iraq. Our analysis shows that incorporating open-source generated conversation and event variables maintains or improves predictive accuracy over traditional variables alone. This work is an important step toward understanding how to leverage organic big data for societal–scale problems.

Publication
In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD)
Kornraphop Kawintiranon
Kornraphop Kawintiranon
Ph.D. Candidate in CS

My research interests include AI/ML and NLP.

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