Demographic Word Embeddings for Racism Detection on Twitter

Mohammed Hasanuzzaman, Gaël Dias, Andy Way
2017 International Joint Conference on Natural Language Processing  
Most social media platforms grant users freedom of speech by allowing them to freely express their thoughts, beliefs, and opinions. Although this represents incredible and unique communication opportunities, it also presents important challenges. Online racism is such an example. In this study, we present a supervised learning strategy to detect racist language on Twitter based on word embedding that incorporate demographic (Age, Gender, and Location) information. Our methodology achieves
more » ... able classification accuracy over a gold standard dataset (F 1 =76.3%) and significantly improves over the classification performance of demographic-agnostic models.
dblp:conf/ijcnlp/HasanuzzamanDW17 fatcat:khjfopdybjblhokhumfl2g4q2i