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Data about migration flows are largely inconsistent across countries, typically outdated, and often inexistent.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.
“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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Estimating countries’ peace index through the lens of the world news as monitored by GDELT
Voukelatou, V., Pappalardo, L., Miliou, I., Gabrielli, L., & Giannotti, F. (2020) · IEEE International Conference on Data Science and Advanced Analytics (DSAA) · doi:10.1109/dsaa49011.2020.00034 · Cited by 18 · see citing works
Challenges when identifying migration from geo-located Twitter data
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Immigration as a Divisive Topic: Clusters and Content Diffusion in the Italian Twitter Debate
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Forecasting U.S. Domestic Migration Using Internet Search Queries
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Estimates of Global Bilateral Migration Flows by Gender between 1960 and 20151
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Happy parents’ tweets: An exploration of Italian Twitter data using sentiment analysis
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- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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