  Text classifiers are applied at scale in the form of one-size-fits-all
solutions. Nevertheless, many studies show that classifiers are biased
regarding different languages and dialects. When measuring and discovering
these biases, some gaps present themselves and should be addressed. First,
``Does language, dialect, and topical content vary across geographical
regions?'' and secondly ``If there are differences across the regions, do they
impact model performance?''. We introduce a novel dataset called GeoOLID with
more than 14 thousand examples across 15 geographically and demographically
diverse cities to address these questions. We perform a comprehensive analysis
of geographical-related content and their impact on performance disparities of
offensive language detection models. Overall, we find that current models do
not generalize across locations. Likewise, we show that while offensive
language models produce false positives on African American English, model
performance is not correlated with each city's minority population proportions.
Warning: This paper contains offensive language.
