  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.
