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A comparison of machine learning approaches for detecting misogynistic Speech in urban dictionary

Lynn, Theo ORCID: 0000-0001-9284-7580, Endo, Patricia Takako ORCID: 0000-0002-9163-5583, Rosati, Pierangelo ORCID: 0000-0002-6070-0426, Silva, Ivanovitch ORCID: 0000-0002-0116-6489, Santos, Guto Leoni ORCID: 0000-0002-0257-4214 and Ging, Debbie ORCID: 0000-0002-6664-5560 (2019) A comparison of machine learning approaches for detecting misogynistic Speech in urban dictionary. In: 2019 International Conference on Cyber Situational Awareness, Data Analytics And Assessment (Cyber SA), 3-4 June 2019, Oxford.

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Abstract

—Recent moves to consider misogyny as a hate crime have refocused efforts for owners of web properties to detect and remove misogynistic speech. This paper considers the use of deep learning techniques for detection of misogyny in Urban Dictionary, a crowdsourced online dictionary for slang words and phrases. We compare the performance of two deep learning techniques, Bi-LSTM and Bi-GRU, to detect misogynistic speech with the performance of more conventional machine learning techniques, logistic regression, Naive-Bayes classification, and Random Forest classification. We find that both deep learning techniques examined have greater accuracy in detecting misogyny in the Urban Dictionary than the other techniques examined. Dublin, Ireland debbie.ging@dcu.ie it was announced that the UK Law Commission would review whether misogynistic conduct should be treated as a hate crime [6]. Index Terms—misogyny, hate speech, recurrent neural networks, deep learning, LSTM, machine learning, urban dictionary

Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:misogyny; hate speech; recurrent neural networks;deep learning; LSTM; machine learning; urban dictionary
Subjects:UNSPECIFIED
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Humanities and Social Science > School of Communications
Published in: 2019 International Conference on Cyber Situational Awareness, Data Analytics And Assessment (Cyber SA). . IEEE.
Publisher:IEEE
Official URL:https://dx.doi.org/10.1109/CyberSA.2019.8899669
Copyright Information:© 2019 The Authors.
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:World Technology Universities Network, Irish Institute of Digital Business
ID Code:26971
Deposited On:05 Apr 2022 11:44 by Thomas Murtagh . Last Modified 05 Apr 2022 11:44

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