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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.<\/jats:p>","DOI":"10.1145\/2528548","type":"journal-article","created":{"date-parts":[[2014,9,17]],"date-time":"2014-09-17T14:22:25Z","timestamp":1410963745000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":135,"title":["Home Location Identification of Twitter Users"],"prefix":"10.1145","volume":"5","author":[{"given":"Jalal","family":"Mahmud","sequence":"first","affiliation":[{"name":"IBM Research -- Almaden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jeffrey","family":"Nichols","sequence":"additional","affiliation":[{"name":"IBM Research -- Almaden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Clemens","family":"Drews","sequence":"additional","affiliation":[{"name":"IBM Research -- Almaden"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2014,7,28]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Proceedings of the 6th International Conference on Weblogs and Social Media (ICWSM\u201912)","author":"Adams Benjamin","year":"2012","unstructured":"Benjamin Adams and Krzysztof Janowicz . 2012 . 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