Location Inference for Non-Geotagged Tweets in User Timelines [Extended Abstract]

This study explores the problem of inferring locations for individual tweets. We scrutinize Twitter user timelines in a novel fashion. First of all, we split each user's tweet timeline temporally into a number of clusters, each tending to imply a distinct location. Subsequently, we adapt machine learning models to our setting and design classifiers that classify each tweet cluster into one of the pre-defined location classes at the city level. Extensive experiments on a large set of real Twitter data suggest that our models are effective at inferring locations for non-geotagged tweets and outperform the state-of-the-art approaches significantly in terms of inference accuracy.