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This paper evaluates large georeferenced corpora, taken from both web-crawledand social media sources, against ground-truth population and language-censusdatasets. The goal is to determine (i) which dataset best represents populationdemographics; (ii) in what parts of the world the datasets are mostrepresentative of actual populations; and (iii) how to weight the datasets toprovide more accurate representations of underlying populations. The paperfinds that the two datasets represent very different populations and that theycorrelate with actual populations with values of r=0.60 (social media) andr=0.49 (web-crawled). Further, Twitter data makes better predictions about theinventory of languages used in each country.

Mapping Languages and Demographics with Georeferenced Corpora