Understanding international migration using tensor factorization

Hieu Nguyen, Kiran Garimella

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

58 Downloads (Pure)


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
Number of pages2
ISBN (Electronic)9781450349147
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


ConferenceInternational World Wide Web Conference
Abbreviated titleWWW


Dive into the research topics of 'Understanding international migration using tensor factorization'. Together they form a unique fingerprint.

Cite this