Demographic word embeddings for racism detection on Twitter
Hasanuzzaman, MohammedORCID: 0000-0003-1838-0091, Dias, Gaël and Way, AndyORCID: 0000-0001-5736-5930
(2017)
Demographic word embeddings for racism detection on Twitter.
In: 8th International Joint Conference on Natural Language Processing, 27 Nov- 1 Dec 2017, Taipei, Taiwan.
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.
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Funders:
ADAPT Centre for Digital Content Technology is funded under the SFI Research Centres Programme (Grant 13/RC/2106) and is co-funded under the European Regional Development Fund.
ID Code:
23334
Deposited On:
21 May 2019 15:44 by
Thomas Murtagh
. Last Modified 04 Jan 2021 16:57