2013/05/16 by Reid Priedhorsky, Aron Culotta, Sara Y. Del Valle · 1 voice
Computer Science · Medicine · Social Sciences · #Data-Driven Disease Surveillance #Geographic Information Systems Studies #Human Mobility and Location-Based Analysis #cs.HC #cs.LG #cs.SI
paper · pdf · doi:10.1145/2531602.2531607
14 pages, 6 figures. Version 2: Move mathematics to appendix, 2 new references, various other presentation improvements. Version 3: Various presentation improvements, accepted at ACM CSCW 2014
arxiv published 2013/05/16 · arxiv created 2013/11/16 · arxiv updated 2013/11/16 · openalex publication_date 2014/02/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
Social Internet content plays an increasingly critical role in many domains, including public health, disaster management, and politics. However, its utility is limited by missing geographic information; for example, fewer than 1.6% of Twitter messages (tweets) contain a geotag. We propose a scalable, content-based approach to estimate the location of tweets using a novel yet simple variant of gaussian mixture models. Further, because real-world applications depend on quantified uncertainty for such estimates, we propose novel metrics of accuracy, precision, and calibration, and we evaluate our approach accordingly. Experiments on 13 million global, comprehensively multi-lingual tweets show that our approach yields reliable, well-calibrated results competitive with previous computationally intensive methods. We also show that a relatively small number of training data are required for good estimates (roughly 30,000 tweets) and models are quite time-invariant (effective on tweets many weeks newer than the training set). Finally, we show that toponyms and languages with small geographic footprint provide the most useful location signals.