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Social Internet content plays an increasingly critical role in many domains,including public health, disaster management, and politics. However, itsutility is limited by missing geographic information; for example, fewer than1.6% of Twitter messages (tweets) contain a geotag. We propose a scalable,content-based approach to estimate the location of tweets using a novel yetsimple variant of gaussian mixture models. Further, because real-worldapplications depend on quantified uncertainty for such estimates, we proposenovel metrics of accuracy, precision, and calibration, and we evaluate ourapproach accordingly. Experiments on 13 million global, comprehensivelymulti-lingual tweets show that our approach yields reliable, well-calibratedresults competitive with previous computationally intensive methods. We alsoshow that a relatively small number of training data are required for goodestimates (roughly 30,000 tweets) and models are quite time-invariant(effective on tweets many weeks newer than the training set). Finally, we showthat toponyms and languages with small geographic footprint provide the mostuseful location signals.

Inferring the Origin Locations of Tweets with Quantitative Confidence