  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, however, has been to describe these corpora themselves rather
than to make inferences about underlying populations. This paper shows that a
difference-in-differences method based on the Herfindahl-Hirschman Index can
identify the bias in digital corpora that is introduced by non-local
populations. These methods tell us where significant changes have taken place
and whether this leads to increased or decreased diversity. This is an
important step in aligning digital corpora like social media with the
real-world populations that have produced them.
