  Current methods to identify and classify racist language in text rely on
small-n qualitative approaches or large-n approaches focusing exclusively on
overt forms of racist discourse. This article provides a step-by-step
generalizable guideline to identify and classify different forms of racist
discourse in large corpora. In our approach, we start by conceptualizing racism
and its different manifestations. We then contextualize these racist
manifestations to the time and place of interest, which allows researchers to
identify their discursive form. Finally, we apply XLM-RoBERTa (XLM-R), a
cross-lingual model for supervised text classification with a cutting-edge
contextual understanding of text. We show that XLM-R and XLM-R-Racismo, our
pretrained model, outperform other state-of-the-art approaches in classifying
racism in large corpora. We illustrate our approach using a corpus of tweets
relating to the Ecuadorian ind\'igena community between 2018 and 2021.
