Detecting racial stereotypes: An Italian social media corpus where psychology meets NLP. Issue 1 (January 2023) - Bosco, Cristina; Patti, Viviana; Frenda, Simona; Cignarella, Alessandra Teresa; Paciello, Marinella; D'Errico, Francesca
Abstract: The generation of stereotypes allows us to simplify the cognitive complexity we have to deal with in everyday life. Stereotypes are extensively used to describe people who belong to a different ethnic group, particularly in racial hoaxes and hateful content against immigrants. This paper addresses the study of stereotypes from a novel perspective that involves psychology and computational linguistics both. On the one hand, it describes an Italian social media corpus built within a social psychology study, where stereotypes and related forms of discredit were made explicit through annotation. On the other hand, it provides some lexical analysis, to bring out the linguistic features of the messages collected in the corpus, and experiments for validating this annotation scheme and its automatic application to other corpora in the future. The main expected outcome is to shed some light on the usefulness of this scheme for training tools that automatically detect and label stereotypes in Italian. Highlights: The paper offers a psychological and computational perspective on racial stereotype. It provides a novel fine-grained annotation scheme for racial stereotype. It sheds light on how to describe and model stereotypes in hoaxes against immigrants. A novel Italian corpus from FaceBook is annotated for validating this scheme. The dataset is lexically analyzed and used for training classifiers.