Towards real-time population estimates: introducing Twitter daily estimates of residents and non-residents at the county level

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 residents/non-residents at the county level and can accurately reflect regular (seasonal tourism) and non-regular events such as the Great American Solar Eclipse of 2017. The findings demonstrate that Twitter holds potential to introduce the dynamic component often lacking in population estimates.