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
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Data about migration flows are largely inconsistent across countries, typically outdated, and often inexistent.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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“many social data analyses rely on machine learned classifiers to classify first, and count later (e.g., Mislove et al., 2011; Zagheni et al., 2014; Abbar et al., 2015).”
From Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019) · cites this paper as Zagheni et al., 2014
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Cites
Understanding individual human mobility patterns
González, M. C., Hidalgo, C. A., & Barabási, A.-Ĺ. (2008) · Nature · doi:10.1038/nature06958 · Cited by 6,186
A universal model for mobility and migration patterns
Simini, F., González, M. C., Maritan, A., & Barabási, A.-Ĺ. (2012) · Nature · doi:10.1038/nature10856 · Cited by 1,480
The P 1 P 2 D Hypothesis: On the Intercity Movement of Persons
Zipf, G. (1946) · American Sociological Review · doi:10.2307/2087063 · Cited by 1,097
Uncovering individual and collective human dynamics from mobile phone records
Candia, J., González, M. C., Wang, P., Schoenharl, T., Madey, G., & Barabási, A.-Ĺ. (2008) · Journal of Physics A Mathematical and Theoretical · doi:10.1088/1751-8113/41/22/224015 · Cited by 611
An Empirical Study of Geographic User Activity Patterns in Foursquare
Noulas, A., Scellato, S., Cecilia Mascolo, C., & Pontil, M. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v5i1.14175 · Cited by 490
The outlook for population growth
Lee, R. (2011) · Science · doi:10.1126/science.1208859 · Cited by 415
Zamal, F. A., Liu, W. J., & Ruths, D. A. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v6i1.14340 · Cited by 310
Automatic construction of travel itineraries using social breadcrumbs
Choudhury, M. D., Feldman, M., Amer-Yahia, S., Golbandi, N., Lempel, R., & Yu, C. (2010) · ACM Conference on Hypertext and Social Media (HT) · doi:10.1145/1810617.1810626 · Cited by 307
Bayesian probabilistic population projections for all countries
Raftery, A. E., Li, N., Ševčíková, H., Gerland, P., & Heilig, G. K. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1211452109 · Cited by 298
Inferring patterns of internal migration from mobile phone call records: evidence from Rwanda
Blumenstock, J. E. (2012) · Information Technology for Development · doi:10.1080/02681102.2011.643209 · Cited by 166
Extracting urban patterns from location-based social networks
Ferrari, L., Alberto Rosi, A., Mamei, M., & Zambonelli, F. (2011) · Workshop on Location-based Social Networks · doi:10.1145/2063212.2063226 · Cited by 147
de Beer, J., Raymer, J., van der Erf, R., & van Wissen, L. (2010) · European Journal of Population / Revue européenne de Démographie · doi:10.1007/s10680-010-9220-z · Cited by 119
International migration beyond gravity: A statistical model for use in population projections
Cohen, J. E., Roig, M., Reuman, D. C., & GoGwilt, C. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0808185105 · Cited by 118
Estimating global migration flow tables using place of birth data
Abel, G. (2013) · Demographic Research · doi:10.4054/demres.2013.28.18 · Cited by 107
Zagheni, E., & Weber, I. G. (2012) · ACM Web Science Conference (WebSci) · doi:10.1145/2380718.2380764 · Cited by 102
Self-citationDiscovering spatiotemporal mobility profiles of cellphone users
Bayır, M. A., Demirbaş, M., & Eagle, N. (2009) · IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM) · doi:10.1109/wowmom.2009.5282489 · Cited by 98
The Hidden Image of the City: Sensing Community Well-Being from Urban Mobility
Lathia, N., Quercia, D., & Crowcroft, J. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-31205-2_6 · Cited by 84
Bayesian Probabilistic Projection of International Migration
Azose, J. J., & Raftery, A. E. (2015) · Demography · doi:10.1007/s13524-015-0415-0 · Cited by 72
Studying inter-national mobility through IP geolocation
State, B., Weber, I. G., & Zagheni, E. (2013) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/2433396.2433432 · Cited by 61
Self-citationDiscovering daily routines from Google Latitude with topic models
Ferrari, L., & Mamei, M. (2011) · IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) · doi:10.1109/percomw.2011.5766928 · Cited by 49
Smith-Clarke, C., Quercia, D., & Capra, L. (2013) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/2441776.2441852 · Cited by 40
Planet-scale human mobility measurement
Hui, P., Mortier, R., Piórkowski, M., Henderson, T., & Crowcroft, J. (2010) · doi:10.1145/1834616.1834618 · Cited by 22
Using mobile phone data to measure the ties between nations
Blumenstock, J. E. (2011) · iConference · doi:10.1145/1940761.1940788 · Cited by 16
Pultar, E., & Raubal, M. (2009) · doi:10.1145/1629890.1629909 · Cited by 13
How to tell an airport from a home
Pitsillidis, A., Xie, Y., Yu, F., Abadi, M., Voelker, G. M., & Savage, S. (2010) · ACM Workshop on Hot Topics in Networks (HotNets) · doi:10.1145/1868447.1868460 · Cited by 5
Research works printed but not linked: 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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Sorted by citations
Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries
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“…bias, under strong assumptions; including matched analysis (De Choudhury et al., 2016; Sharma and Cosley, 2016), instrumental variables analysis (Sharma et al., 2015), regression discontinuities (Malik and Pfeffer, 2016), and differences-in-differences (Carmi et al., 2012; Zagheni et al., 2014).”
BackgroundMethodThe Misgendering Machines: Trans/HCI Implications of Automatic Gender Recognition
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Zagheni, E., Weber, I. G., & Gummadi, K. P. (2017) · Population and Development Review · doi:10.1111/padr.12102 · Cited by 200
Self-citationWhat’s to Like? Facebook as a Tool for Survey Data Collection
Schneider, D., & Harknett, K. (2019) · Sociological Methods & Research · doi:10.1177/0049124119882477 · Cited by 192
Hoffmann, R., Šedová, B., & Vinke, K. (2021) · Global Environmental Change · doi:10.1016/j.gloenvcha.2021.102367 · Cited by 159
“At the same time, social media, such as Facebook or Twitter, provide innovative ways to learn about migration pathways and the profiles of migrants (see e.g., Blumenstock 2012; Chi et al. 2020; Spyratos et al. 2019; Zagheni et al. 2014).”
Background
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)
- Venue
- Proceedings of the 23rd International Conference on World Wide Web
- Publisher
- ACM
- Published
- 2014-04-07
- Pages
- 439-444
- 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
- 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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