  In many Twitter studies, it is important to know where a tweet came from in
order to use the tweet content to study regional user behavior. However,
researchers using Twitter to understand user behavior often lack sufficient
geo-tagged data. Given the huge volume of Twitter data there is a need for
accurate automated geolocating solutions. Herein, we present a new method to
predict a Twitter user's location based on the information in a single tweet.
We integrate text and user profile meta-data into a single model using a
convolutional neural network. Our experiments demonstrate that our neural model
substantially outperforms baseline methods, achieving 52.8% accuracy and 92.1%
accuracy on city-level and country-level prediction respectively.
