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In recent years, the increasing propagation of hate speech on social mediaand the urgent need for effective counter-measures have drawn significantinvestment from governments, companies, and researchers. A large number ofmethods have been developed for automated hate speech detection online. Thisaims to classify textual content into non-hate or hate speech, in which casethe method may also identify the targeting characteristics (i.e., types ofhate, such as race, and religion) in the hate speech. However, we noticesignificant difference between the performance of the two (i.e., non-hate v.s.hate). In this work, we argue for a focus on the latter problem for practicalreasons. We show that it is a much more challenging task, as our analysis ofthe language in the typical datasets shows that hate speech lacks unique,discriminative features and therefore is found in the 'long tail' in a datasetthat is difficult to discover. We then propose Deep Neural Network structuresserving as feature extractors that are particularly effective for capturing thesemantics of hate speech. Our methods are evaluated on the largest collectionof hate speech datasets based on Twitter, and are shown to be able tooutperform the best performing method by up to 5 percentage points inmacro-average F1, or 8 percentage points in the more challenging case ofidentifying hateful content.

Hate Speech Detection: A Solved Problem? The Challenging Case of Long Tail on Twitter