Type

Conference Proceedings

Authors

Andy Way
Gaël Dias
Mohammed Hasanuzzaman

Subjects

Language

Topics
detection demographic supervised learning information age twitter social media machine translating racism

Demographic word embeddings for racism detection on Twitter (2017)

Abstract 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.
Collections Ireland -> Dublin City University -> Publication Type = Conference or Workshop Item
Ireland -> Dublin City University -> DCU Faculties and Centres = DCU Faculties and Schools: Faculty of Engineering and Computing: School of Computing
Ireland -> Dublin City University -> DCU Faculties and Centres = Research Initiatives and Centres: ADAPT
Ireland -> Dublin City University -> Status = Published
Ireland -> Dublin City University -> Subject = Computer Science: Machine translating

Full list of authors on original publication

Andy Way, Gaël Dias, Mohammed Hasanuzzaman

Experts in our system

1
Andy Way
Dublin City University
Total Publications: 229
 
2
Mohammed Hasanuzzaman
Dublin City University
Total Publications: 9