  The goal of this paper is to provide a complete representation of regional
linguistic variation on a global scale. To this end, the paper focuses on
removing three constraints that have previously limited work within
dialectology/dialectometry. First, rather than assuming a fixed and incomplete
set of variants, we use Computational Construction Grammar to provide a
replicable and falsifiable set of syntactic features. Second, rather than
assuming a specific area of interest, we use global language mapping based on
web-crawled and social media datasets to determine the selection of national
varieties. Third, rather than looking at a single language in isolation, we
model seven major languages together using the same methods: Arabic, English,
French, German, Portuguese, Russian, and Spanish. Results show that models for
each language are able to robustly predict the region-of-origin of held-out
samples better using Construction Grammars than using simpler syntactic
features. These global-scale experiments are used to argue that new methods in
computational sociolinguistics are able to provide more generalized models of
regional variation that are essential for understanding language variation and
change at scale.
