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arXiv · 1702.04996

Understanding International Migration using Tensor Factorization

Hieu Nguyen·Kiran Garimella
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Abstract

Understanding human migration is of great interest to demographers and social scientists. User generated digital data has made it easier to study such patterns at a global scale. Geo coded Twitter data, in particular, has been shown to be a promising source to analyse large scale human migration. But given the scale of these datasets, a lot of manual effort has to be put into processing and getting actionable insights from this data. In this paper, we explore feasibility of using a new tool, tensor decomposition, to understand trends in global human migration. We model human migration as a three mode tensor, consisting of (origin country, destination country, time of migration) and apply CP decomposition to get meaningful low dimensional factors. Our experiments on a large Twitter dataset spanning 5 years and over 100M tweets show that we can extract meaningful migration patterns.

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Hieu Nguyen, Kiran Garimella. 2017-02-16. Understanding International Migration using Tensor Factorization. https://arxiv.org/abs/1702.04996

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