  We present a new algorithm for inferring the home location of Twitter users
at different granularities, including city, state, time zone or geographic
region, using the content of users tweets and their tweeting behavior. Unlike
existing approaches, our algorithm uses an ensemble of statistical and
heuristic classifiers to predict locations and makes use of a geographic
gazetteer dictionary to identify place-name entities. We find that a
hierarchical classification approach, where time zone, state or geographic
region is predicted first and city is predicted next, can improve prediction
accuracy. We have also analyzed movement variations of Twitter users, built a
classifier to predict whether a user was travelling in a certain period of time
and use that to further improve the location detection accuracy. Experimental
evidence suggests that our algorithm works well in practice and outperforms the
best existing algorithms for predicting the home location of Twitter users.
