# Understanding International Migration using Tensor Factorization

## TL;DR

This paper demonstrates that tensor decomposition can effectively analyze large-scale Twitter data to uncover meaningful global human migration patterns over time.

## Contribution

It introduces a novel application of tensor factorization to model and analyze international migration using geo-tagged social media data.

## Key findings

- Successfully modeled migration as a three-mode tensor.
- Extracted meaningful migration patterns from 5 years of Twitter data.
- Showed tensor decomposition's potential for large-scale migration analysis.

## 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.