论文标题
将社交媒体和调查数据结合起来，以播放美国的移民股票
Combining social media and survey data to nowcast migrant stocks in the United States
论文作者
论文摘要
衡量和预测迁移模式以及它们如何随着时间的变化，对了解更广泛的人口趋势具有重要意义，可有效地设计政策和分配资源。但是，通常缺乏有关迁移和流动性的数据，并且确实存在的数据并不及时可用。社交媒体数据提供了新的机会，可以提供更多最新的人口统计估计并补充更多传统的数据源。例如，可以将Facebook视为定期更新的大型数字普查。但是，其用户并不代表潜在的人群。本文提出了一个统计框架，将社交媒体数据与传统的调查数据相结合，以及时生产美国国家的移民股票的“现象”。该模型结合了Facebook数据的偏差调整以及汇总的主组件时间序列方法，以说明年龄，时间和空间之间的相关性。我们说明了来自墨西哥，印度和德国的移民的结果，并表明该模型的表现优于仅依赖社交媒体或调查数据的替代方案。
Measuring and forecasting migration patterns, and how they change over time, has important implications for understanding broader population trends, for designing policy effectively and for allocating resources. However, data on migration and mobility are often lacking, and those that do exist are not available in a timely manner. Social media data offer new opportunities to provide more up-to-date demographic estimates and to complement more traditional data sources. Facebook, for example, can be thought of as a large digital census that is regularly updated. However, its users are not representative of the underlying population. This paper proposes a statistical framework to combine social media data with traditional survey data to produce timely `nowcasts' of migrant stocks by state in the United States. The model incorporates bias adjustment of the Facebook data, and a pooled principal component time series approach, to account for correlations across age, time and space. We illustrate the results for migrants from Mexico, India and Germany, and show that the model outperforms alternatives that rely solely on either social media or survey data.
