Abstract. Computational measures of linguistic diversity help us understand the linguistic landscape using digital language data. The contribution of this paper is to calibrate measures of linguistic diversity using restrictions on international travel resulting from the COVID-19 pandemic. Previous work has mapped the distribution of languages using geo-referenced social media and web data. The goal, … More Measuring Linguistic Diversity During COVID-19

[Draft: Corpus_of_Global_Language_Use.Revisions.Preprint] [Data: The Corpus of Global Language Use v.4.2] Abstract This paper describes a web-based corpus of global language use with a focus on how this corpus can be used for data-driven language mapping. First, the corpus provides a representation of where national varieties of major languages are used (e.g., English, Arabic, Russian) together … More Mapping Languages: The Corpus of Global Language Use

[Read draft here: LREC_2020] [GeoWAC Corpus Family] Abstract While text corpora have been steadily increasing in overall size, even very large corpora are not designed to represent global population demographics. For example, recent work has shown that existing English gigaword corpora over-represent inner-circle varieties from the US and the UK (Dunn, 2019b). To correct implicit … More Geographically-Balanced Gigaword Corpora for 50 Language Varieties

[Read full-text] Co-authored with Ben Adams. Abstract This paper evaluates large georeferenced corpora, taken from both web-crawled and social media sources, against ground-truth population and language-census datasets. The goal is to determine (i) which dataset best represents population demographics; (ii) in what parts of the world the datasets are most representative of actual populations; and … More Mapping Languages and Demographics with Georeferenced Corpora