  Accurate estimation of user location is important for many online services.
Previous neural network based methods largely ignore the hierarchical structure
among locations. In this paper, we propose a hierarchical location prediction
neural network for Twitter user geolocation. Our model first predicts the home
country for a user, then uses the country result to guide the city-level
prediction. In addition, we employ a character-aware word embedding layer to
overcome the noisy information in tweets. With the feature fusion layer, our
model can accommodate various feature combinations and achieves
state-of-the-art results over three commonly used benchmarks under different
feature settings. It not only improves the prediction accuracy but also greatly
reduces the mean error distance.
