  The purpose of this paper is to ascertain the influence of sociocultural
factors (i.e., social, cultural, and political) in the development of hate
speech detection systems. We set out to investigate the suitability of using
open-source training data to monitor levels of anti-LGBTQ+ content on social
media across different national-varieties of English. Our findings suggests the
social and cultural alignment of open-source hate speech data sets influences
the predicted outputs. Furthermore, the keyword-search approach of anti-LGBTQ+
slurs in the development of open-source training data encourages detection
models to overfit on slurs; therefore, anti-LGBTQ+ content may go undetected.
We recommend combining empirical outputs with qualitative insights to ensure
these systems are fit for purpose.
