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Although there is an unprecedented effort to provide adequate responses interms of laws and policies to hate content on social media platforms, dealingwith hatred online is still a tough problem. Tackling hate speech in thestandard way of content deletion or user suspension may be charged withcensorship and overblocking. One alternate strategy, that has received littleattention so far by the research community, is to actually oppose hate contentwith counter-narratives (i.e. informed textual responses). In this paper, wedescribe the creation of the first large-scale, multilingual, expert-baseddataset of hate speech/counter-narrative pairs. This dataset has been builtwith the effort of more than 100 operators from three different NGOs thatapplied their training and expertise to the task. Together with the collecteddata we also provide additional annotations about expert demographics, hate andresponse type, and data augmentation through translation and paraphrasing.Finally, we provide initial experiments to assess the quality of our data.

CONAN -- COunter NArratives through Nichesourcing: a Multilingual Dataset of Responses to Fight Online Hate Speech