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Where are You Tweeting?: A Context and User Movement Based Approach

Geotagged tweets allow one to extract geo-information-trend, search local events, and identify natural disasters. In this paper, we propose a Hidden-Markov-based model to integrate tweet contents and user movements for geotagging. A language model is obtained for different locations from training datasets and movements of users among cities are analyzed. Home cities of users are considered in modeling the patterns of user movements. Evaluation on a large Twitter dataset shows that our method can significantly improve geotagging accuracy by 55% for home cities and 2% for other non-home cities as well as reduce error distances by orders of magnitude compared with pure text-based methods.