Towards real-time population estimates: introducing Twitter daily estimates of residents and non-residents at the county level
Yago Martin, Zhenlong Li, Yue Ge

TL;DR
This paper presents a method to estimate county-level populations in near real-time using geotagged Twitter data, enabling more dynamic and accurate demographic insights compared to traditional methods.
Contribution
It introduces a novel Twitter-based population estimation approach that provides daily counts of residents and non-residents at the county level with high accuracy.
Findings
Over 80% accuracy in home location identification
Strong correlation with official population statistics
Effectively captures seasonal and event-driven population changes
Abstract
The study of migrations and mobility has historically been severely limited by the absence of reliable data or the temporal sparsity of the available data. Using geospatial digital trace data, the study of population movements can be much more precisely and dynamically measured. Our research seeks to develop a near real-time (one-day lag) Twitter census that gives a more temporally granular picture of local and non-local population at the county level. Leveraging geotagged tweets to determine the home location of all active Twitter users, we contribute to the field of digital and computational demography by obtaining accurate daily Twitter population stocks (residents and non-residents). Internal validation reveals over 80% of accuracy when compared with users self-reported home location. External validation results suggest these stocks correlate with available statistics of…
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Taxonomy
TopicsHuman Mobility and Location-Based Analysis · Data-Driven Disease Surveillance · Urban Transport and Accessibility
Methods7 Fastest Ways to Call American Airlines Reservations Number (USA Guide)
