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.

翻译：利用地理空间数字跟踪数据,对人口流动的研究可以更加精确和动态地衡量。我们的研究寻求开发近实时(一天滞后)推特普查,以提供州一级当地和非当地人口的时间性更小的图象。利用地理标签推文来确定所有活跃的推特用户的住址,我们通过获取准确的每日推特人口存量(居民和非居民),为数字和计算人口统计领域作出贡献。内部验证显示,与用户自我报告的家庭所在地相比,准确率超过80%。外部验证结果表明,这些存量与县一级居民/非居民的现有统计数据相关,可以准确反映正常(海上旅游)和非经常事件,如2017年大美国太阳能日落叶。研究结果表明,Twitter有可能引入人口估计数中通常缺乏的动态部分。