  We present a new computational technique to detect and analyze statistically
significant geographic variation in language. Our meta-analysis approach
captures statistical properties of word usage across geographical regions and
uses statistical methods to identify significant changes specific to regions.
While previous approaches have primarily focused on lexical variation between
regions, our method identifies words that demonstrate semantic and syntactic
variation as well.
  We extend recently developed techniques for neural language models to learn
word representations which capture differing semantics across geographical
regions. In order to quantify this variation and ensure robust detection of
true regional differences, we formulate a null model to determine whether
observed changes are statistically significant. Our method is the first such
approach to explicitly account for random variation due to chance while
detecting regional variation in word meaning.
  To validate our model, we study and analyze two different massive online data
sets: millions of tweets from Twitter spanning not only four different
countries but also fifty states, as well as millions of phrases contained in
the Google Book Ngrams. Our analysis reveals interesting facets of language
change at multiple scales of geographic resolution -- from neighboring states
to distant continents.
  Finally, using our model, we propose a measure of semantic distance between
languages. Our analysis of British and American English over a period of 100
years reveals that semantic variation between these dialects is shrinking.
