Conference Publication Details
Mandatory Fields
Mohammed Hasanuzzaman, GaƩl Dias and Andy Way
IJCNLP 2017 - 8th International Joint Conference on Natural Language Processing
Demographic Word Embeddings for Racism Detection on Twitter
2017
November
Published
1
()
Optional Fields
926
936
Taiwan
27-NOV-17
01-DEC-17
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 reasonable classification accuracy over a gold standard dataset (F1=76.3%) and significantly improves over the classification performance of demographic-agnostic models.
http://www.aclweb.org/anthology/I17-1093
Grant Details