  Real-time location inference of social media users is the fundamental of some
spatial applications such as localized search and event detection. While tweet
text is the most commonly used feature in location estimation, most of the
prior works suffer from either the noise or the sparsity of textual features.
In this paper, we aim to tackle these two problems. We use topic modeling as a
building block to characterize the geographic topic variation and lexical
variation so that "one-hot" encoding vectors will no longer be directly used.
We also incorporate other features which can be extracted through the Twitter
streaming API to overcome the noise problem. Experimental results show that our
RATE algorithm outperforms several benchmark methods, both in the precision of
region classification and the mean distance error of latitude and longitude
regression.
