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Detection of hate speech has been formulated as a standalone application of
NLP and different approaches have been adopted for identifying the target
groups, obtaining raw data, defining the labeling process, choosing the
detection algorithm, and evaluating the performance in the desired setting.
However, unlike other downstream tasks, hate speech suffers from the lack of
large-sized, carefully curated, generalizable datasets owing to the highly
subjective nature of the task. In this paper, we first analyze the issues
surrounding hate speech detection through a data-centric lens. We then outline
a holistic framework to encapsulate the data creation pipeline across seven
broad dimensions by taking the specific example of hate speech towards sexual
minorities. We posit that practitioners would benefit from following this
framework as a form of best practice when creating hate speech datasets in the
future.
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