Using twitter data to estimate the relationships between short-term mobility and long-term migration

Lee Fiorio, Emilio Zagheni, Guy Abel, Ingmar Weber, Jixuan Cai, Guillermo Vinué

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

Abstract

Migration estimates are sensitive to definitions of time interval and duration. For example, when does a tourist become a migrant? As a result, harmonizing across different kinds of estimates or data sources can be difficult. Moreover in countries like the United States, that do not have a national registry system, estimates of internal migration typically rely on survey data that can require over a year from data collection to publication. In addition, each survey can ask only a limited set questions about migration (e.g., where did you live a year ago? where did you live five years ago?). We leverage a sample of geo-referenced Twitter tweets for about 62,000 users, spanning the period between 2010 and 2016, to estimate a series of US internal migration flows under varying time intervals and durations. Our findings, expressed in terms of 'migration curves', document, for the first time, the relationships between short-term mobility and long-term migration. The results open new avenues for demographic research. More specifically, future directions include the use of migration curves to produce probabilistic estimates of long-term migration from short-term (and vice versa) and to nowcast mobility rates at different levels of spatial and temporal granularity using a combination of previously published American Community Survey data and up-to-date data from a panel of Twitter users.

Original languageEnglish
Title of host publicationWebSci 2017 - Proceedings of the 2017 ACM Web Science Conference
PublisherAssociation for Computing Machinery, Inc
Pages103-110
Number of pages8
ISBN (Electronic)9781450348966
DOIs
Publication statusPublished - 25 Jun 2017
Event9th ACM Web Science Conference, WebSci 2017 - Troy, United States
Duration: 25 Jun 201728 Jun 2017

Other

Other9th ACM Web Science Conference, WebSci 2017
CountryUnited States
CityTroy
Period25/6/1728/6/17

Keywords

  • Demographic research
  • Migration
  • Mobility
  • Twitter

ASJC Scopus subject areas

  • Computer Networks and Communications

Cite this

Fiorio, L., Zagheni, E., Abel, G., Weber, I., Cai, J., & Vinué, G. (2017). Using twitter data to estimate the relationships between short-term mobility and long-term migration. In WebSci 2017 - Proceedings of the 2017 ACM Web Science Conference (pp. 103-110). Association for Computing Machinery, Inc. https://doi.org/10.1145/3091478.3091496

Using twitter data to estimate the relationships between short-term mobility and long-term migration. / Fiorio, Lee; Zagheni, Emilio; Abel, Guy; Weber, Ingmar; Cai, Jixuan; Vinué, Guillermo.

WebSci 2017 - Proceedings of the 2017 ACM Web Science Conference. Association for Computing Machinery, Inc, 2017. p. 103-110.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Fiorio, L, Zagheni, E, Abel, G, Weber, I, Cai, J & Vinué, G 2017, Using twitter data to estimate the relationships between short-term mobility and long-term migration. in WebSci 2017 - Proceedings of the 2017 ACM Web Science Conference. Association for Computing Machinery, Inc, pp. 103-110, 9th ACM Web Science Conference, WebSci 2017, Troy, United States, 25/6/17. https://doi.org/10.1145/3091478.3091496
Fiorio L, Zagheni E, Abel G, Weber I, Cai J, Vinué G. Using twitter data to estimate the relationships between short-term mobility and long-term migration. In WebSci 2017 - Proceedings of the 2017 ACM Web Science Conference. Association for Computing Machinery, Inc. 2017. p. 103-110 https://doi.org/10.1145/3091478.3091496
Fiorio, Lee ; Zagheni, Emilio ; Abel, Guy ; Weber, Ingmar ; Cai, Jixuan ; Vinué, Guillermo. / Using twitter data to estimate the relationships between short-term mobility and long-term migration. WebSci 2017 - Proceedings of the 2017 ACM Web Science Conference. Association for Computing Machinery, Inc, 2017. pp. 103-110
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