  As pointed out by several scholars, current research on hate speech (HS)
recognition is characterized by unsystematic data creation strategies and
diverging annotation schemata. Subsequently, supervised-learning models tend to
generalize poorly to datasets they were not trained on, and the performance of
the models trained on datasets labeled using different HS taxonomies cannot be
compared. To ease this problem, we propose applying extremely weak supervision
that only relies on the class name rather than on class samples from the
annotated data. We demonstrate the effectiveness of a state-of-the-art
weakly-supervised text classification model in various in-dataset and
cross-dataset settings. Furthermore, we conduct an in-depth quantitative and
qualitative analysis of the source of poor generalizability of HS
classification models.
