Most social media platforms grant users
freedom of speech by allowing them to
freely express their thoughts, beliefs, and
opinions. Although this represents incredible
and unique communication opportunities,
it also presents important challenges.
Online racism is such an example.
In this study, we present a supervised
learning strategy to detect racist language
on Twitter based on word embedding
that incorporate demographic (Age,
Gender, and Location) information. Our
methodology achieves reasonable classification
accuracy over a gold standard
dataset (F1=76.3%) and significantly improves
over the classification performance
of demographic-agnostic models.