  This paper investigates the impact of corpus creation decisions on large
multi-lingual geographic web corpora. Beginning with a 427 billion word corpus
derived from the Common Crawl, three methods are used to improve the quality of
sub-corpora representing specific language-country pairs like New Zealand
English: (i) the agreement of independent language identification systems, (ii)
hash-based deduplication, and (iii) location-specific outlier detection. The
impact of each of these steps is then evaluated at the language level and the
country level by using corpus similarity measures to compare each resulting
corpus with baseline data sets. The goal is to understand the impact of
upstream data cleaning decisions on downstream corpora with a specific focus on
under-represented languages and populations. The evaluation shows that the
validity of sub-corpora is improved with each stage of cleaning but that this
improvement is unevenly distributed across languages and populations. This
result shows how standard corpus creation techniques can accidentally exclude
under-represented populations.
