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HateBR: A Large Expert Annotated Corpus of Brazilian Instagram Comments for Offensive Language and Hate Speech Detection

Francielle Alves VargasIsabelle CarvalhoFabiana Rodrigues de G\'oesFabr\'icio BenevenutoThiago Alexandre Salgueiro Pardo
Mar 2021
摘要
Due to the severity of the social media offensive and hateful comments inBrazil, and the lack of research in Portuguese, this paper provides the firstlarge-scale expert annotated corpus of Brazilian Instagram comments for hatespeech and offensive language detection. The HateBR corpus was collected fromthe comment section of Brazilian politicians' accounts on Instagram andmanually annotated by specialists, reaching a high inter-annotator agreement.The corpus consists of 7,000 documents annotated according to three differentlayers: a binary classification (offensive versus non-offensive comments),offensiveness-level classification (highly, moderately, and slightlyoffensive), and nine hate speech groups (xenophobia, racism, homophobia,sexism, religious intolerance, partyism, apology for the dictatorship,antisemitism, and fatphobia). We also implemented baseline experiments foroffensive language and hate speech detection and compared them with aliterature baseline. Results show that the baseline experiments on our corpusoutperform the current state-of-the-art for the Portuguese language.
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