Understanding international migration using tensor factorization

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Journal Title
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Volume Title
Conference article in proceedings
Date
2019-01-01
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Degree programme
Language
en
Pages
2
829-830
Series
26th International World Wide Web Conference 2017, WWW 2017 Companion
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.
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| openaire: EC/H2020/654024/EU//SoBigData
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Citation
Nguyen , H & Garimella , K 2019 , Understanding international migration using tensor factorization . in 26th International World Wide Web Conference 2017, WWW 2017 Companion . International World Wide Web Conferences Steering Committee , pp. 829-830 , International World Wide Web Conference , Perth , Australia , 03/04/2017 . https://doi.org/10.1145/3041021.3054222