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Cited by 224Human Mobility and Location-Based AnalysisMigration and Labor DynamicsMigration, Health, Geopolitics, Historical Geography

The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.

Inferring international and internal migration patterns from Twitter data

Zagheni, Emilio, Garimella, Venkata Rama Kiran, Weber, Ingmar, State, Bogdan·2014·Proceedings of the 23rd International Conference on World Wide Web
DOI 10.1145/2567948.2576930
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Abstract

Data about migration flows are largely inconsistent across countries, typically outdated, and often inexistent. Despite the importance of migration as a driver of demographic change, there is limited availability of migration statistics. Generally, researchers rely on census data to indirectly estimate flows. However, little can be inferred for specific years between censuses and for recent trends. The increasing availability of geolocated data from online sources has opened up new opportunities to track recent trends in migration patterns and to improve our understanding of the relationships between internal and international migration. In this paper, we use geolocated data for about 500,000 users of the social network website "Twitter". The data are for users in OECD countries during the period May 2011- April 2013. We evaluated, for the subsample of users who have posted geolocated tweets regularly, the geographic movements within and between countries for independent periods of four months, respectively. Since Twitter users are not representative of the OECD population, we cannot infer migration rates at a single point in time. However, we proposed a difference-in-differences approach to reduce selection bias when we infer trends in out-migration rates for single countries. Our results indicate that our approach is relevant to address two longstanding questions in the migration literature. First, our methods can be used to predict turning points in migration trends, which are particularly relevant for migration forecasting. Second, geolocated Twitter data can substantially improve our understanding of the relationships between internal and international migration. Our analysis relies uniquely on publicly available data that could be potentially available in real time and that could be used to monitor migration trends. The Web Science community is well-positioned to address, in future work, a number of methodological and substantive questions that we discuss in this article.

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  1. R. v. d. Erf . Analysis of final results. In Paper prepared for the IMEM progress meeting on 22--24 February in Asker , Norway , 2012 . R. v. d. Erf. Analysis of final results. In Paper prepared for the IMEM progress meeting on 22--24 February in Asker, Norway, 2012.

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Metadata

Authors
Zagheni, E., Garimella, V. R. K., Weber, I., & State, B.
Affiliations
  • Queens College, CUNY (US)
  • Qatar Computing Research Institute, Doha, Qatar
  • Stanford University (US)
ORCID
Venue
Proceedings of the 23rd International Conference on World Wide Web
Publisher
ACM
Published
2014-04-07
Pages
439-444
DOI
10.1145/2567948.2576930
OpenAlex
W199749264
Cited by
224
References
25
Citations by year
2026: 3 · 2025: 9 · 2024: 15 · 2023: 27 · 2022: 31 · 2021: 25 · 2020: 22 · 2019: 24 · 2018: 23 · 2017: 23 · 2016: 10 · 2015: 10 · 2014: 2
FWCI
24.26
Type
conference paper
Language
en
Open access
closed
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations
International World Wide Web Conference (WWW) (publishedVersion)
Topics
  • Social Sciences › Transportation › Human Mobility and Location-Based Analysis
  • Social Sciences › Sociology and Political Science › Migration and Labor Dynamics
  • Social Sciences › Demography › Migration, Health, Geopolitics, Historical Geography
Keywords
migration patterns, internal migration, international migration, migration forecasting, difference-in-differences, selection bias, migration trends, OECD countries, social media data

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