High-resolution home location prediction from tweets using deep learning with dynamic structure

Timely and high-resolution estimates of the home locations of a sufficiently large subset of the population are critical for applications such as disaster response and public health. However, conventional data sources, such as census and surveys, have a substantial time lag and cannot capture seasonal trends. Recently, the large user-base and real-time nature of social media data have been leveraged to address this problem. However, inherent sparsity and noise, along with large estimation uncertainty in home locations, have limited their effectiveness. In this paper, we develop a deep-learning solution that deals with the sparsity and noise of social media data. We obtained over 90% accuracy for large subsets on a commonly used dataset. Systematic comparisons show that our method gives the highest accuracy both for the entire sample and for subsets.