Understanding international migration using tensor factorization

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A4 Artikkeli konferenssijulkaisussa
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2019-01-01

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Department of Computer Science

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en

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2

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26th International World Wide Web Conference 2017, WWW 2017 Companion, pp. 829-830

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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DOI

10.1145/3041021.3054222

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

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https://urn.fi/URN:NBN:fi:aalto-202001021341

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[article-cris] Perustieteiden korkeakoulu / SCI