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.

A 2013 article from NPR's "Code Switch," which explores issues of race and identity, delved into the etymology of the term after it surfaced in George Zimmerman's trial for the killing of Trayvon Martin. Academics and historians interviewed by the article's author, Gene Demby, dated the term's use back to Shakespearean times when it was applied as an "insult for an obnoxious bloviator" and was usually directed at people from Scotland or Ireland. When immigrants from those countries crossed the Atlantic to America, the term followed them. Jelani Cobb, a historian now at Columbia University and a staff writer for the New Yorker interviewed by Demby, noted it was later tied to poor White farm hands "since the manual labor they did involved driving livestock with a whip."