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Well-annotated data is a prerequisite for good Natural Language Processingmodels. Too often, though, annotation decisions are governed by optimizing timeor annotator agreement. We make a case for nuanced efforts in aninterdisciplinary setting for annotating offensive online speech. Detectingoffensive content is rapidly becoming one of the most important real-world NLPtasks. However, most datasets use a single binary label, e.g., for hate orincivility, even though each concept is multi-faceted. This modeling choiceseverely limits nuanced insights, but also performance. We show that a morefine-grained multi-label approach to predicting incivility and hateful orintolerant content addresses both conceptual and performance issues. We releasea novel dataset of over 40,000 tweets about immigration from the US and UK,annotated with six labels for different aspects of incivility and intolerance.Our dataset not only allows for a more nuanced understanding of harmful speechonline, models trained on it also outperform or match performance on benchmarkdatasets.

"It's Not Just Hate'': A Multi-Dimensional Perspective on Detecting Harmful Speech Online