Understanding international migration using tensor factorization

Hieu Nguyen, Kiran Garimella

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

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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 the the 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.

Original languageEnglish
Title of host publication26th International World Wide Web Conference 2017, WWW 2017 Companion
Pages829-830
Number of pages2
ISBN (Electronic)9781450349147
DOIs
Publication statusPublished - 1 Jan 2019
MoE publication typeA4 Article in a conference publication
EventInternational World Wide Web Conference - Perth, Australia
Duration: 3 Apr 20177 Apr 2017
Conference number: 26

Conference

ConferenceInternational World Wide Web Conference
Abbreviated titleWWW
Country/TerritoryAustralia
CityPerth
Period03/04/201707/04/2017

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