Measuring Linguistic Diversity During COVID-19
This work addresses the issue of aligning digital corpora with real-world populations for researchers in computational linguistics, though it is incremental as it builds on existing methods.
The paper tackled the problem of bias in digital language corpora due to non-local populations by calibrating linguistic diversity measures using COVID-19 travel restrictions, showing that a difference-in-differences method based on the Herfindahl-Hirschman Index can identify such biases.
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.