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Categorizing Sexism and Misogyny through Neural Approaches

Authors:

Vasudeva VarmaAuthors Info & Claims

ACM Transactions on the Web (TWEB), Volume 15, Issue 4

Article No.: 17, Pages 1 - 31

https://doi.org/10.1145/3457189

Published: 14 June 2021 Publication History

Abstract

Sexism, an injustice that subjects women and girls to enormous suffering, manifests in blatant as well as subtle ways. In the wake of growing documentation of experiences of sexism on the web, the automatic categorization of accounts of sexism has the potential to assist social scientists and policymakers in studying and thereby countering sexism. The existing work on sexism classification has certain limitations in terms of the categories of sexism used and/or whether they can co-occur. To the best of our knowledge, this is the first work on the multi-label classification of sexism of any kind(s).¹ We also consider the related task of misogyny classification. While sexism classification is performed on textual accounts describing sexism suffered or observed, misogyny classification is carried out on tweets perpetrating misogyny. We devise a novel neural framework for classifying sexism and misogyny that can combine text representations obtained using models such as Bidirectional Encoder Representations from Transformers with distributional and linguistic word embeddings using a flexible architecture involving recurrent components and optional convolutional ones. Further, we leverage unlabeled accounts of sexism to infuse domain-specific elements into our framework. To evaluate the versatility of our neural approach for tasks pertaining to sexism and misogyny, we experiment with adapting it for misogyny identification. For categorizing sexism, we investigate multiple loss functions and problem transformation techniques to address the multi-label problem formulation. We develop an ensemble approach using a proposed multi-label classification model with potentially overlapping subsets of the category set. Proposed methods outperform several deep-learning as well as traditional machine learning baselines for all three tasks.

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Cited By

Rehman MZahoor SManzoor AMaqbool MKumar N(2025)A context-aware attention and graph neural network-based multimodal framework for misogyny detectionInformation Processing & Management10.1016/j.ipm.2024.10389562:1(103895)Online publication date: Jan-2025
https://doi.org/10.1016/j.ipm.2024.103895
Lei WAbdullah NSyed Aris S(2024)A Systematic Literature Review on Automatic Sexism Detection in Social MediaEngineering, Technology & Applied Science Research10.48084/etasr.888114:6(18178-18188)Online publication date: 2-Dec-2024
https://doi.org/10.48084/etasr.8881
Turner A(2024)Sexism and misogyny as traits of police culture: Problems, red flags and solutionsInternational Journal of Police Science & Management10.1177/1461355724122873626:2(279-291)Online publication date: 7-Feb-2024
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Index Terms

Categorizing Sexism and Misogyny through Neural Approaches

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Abstract
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cover image ACM Transactions on the Web

ACM Transactions on the Web Volume 15, Issue 4

November 2021

152 pages

ISSN:1559-1131

EISSN:1559-114X

DOI:10.1145/3465465

Editor:
Brian D. Davison
Lehigh University, USA

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Copyright © 2021 Association for Computing Machinery.

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Publication History

Published: 14 June 2021

Accepted: 01 March 2021

Revised: 01 December 2020

Received: 01 May 2020

Published in TWEB Volume 15, Issue 4

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Cited By

Rehman MZahoor SManzoor AMaqbool MKumar N(2025)A context-aware attention and graph neural network-based multimodal framework for misogyny detectionInformation Processing & Management10.1016/j.ipm.2024.10389562:1(103895)Online publication date: Jan-2025
https://doi.org/10.1016/j.ipm.2024.103895
Lei WAbdullah NSyed Aris S(2024)A Systematic Literature Review on Automatic Sexism Detection in Social MediaEngineering, Technology & Applied Science Research10.48084/etasr.888114:6(18178-18188)Online publication date: 2-Dec-2024
https://doi.org/10.48084/etasr.8881
Turner A(2024)Sexism and misogyny as traits of police culture: Problems, red flags and solutionsInternational Journal of Police Science & Management10.1177/1461355724122873626:2(279-291)Online publication date: 7-Feb-2024
https://doi.org/10.1177/14613557241228736
Sultana SBegum Kali M(2024)Exploring ChatGPT for identifying sexism in the communication of software developersProceedings of the 17th International Conference on PErvasive Technologies Related to Assistive Environments10.1145/3652037.3663918(400-403)Online publication date: 26-Jun-2024
https://dl.acm.org/doi/10.1145/3652037.3663918
Wu XWu HHe P(2024)Leveraging Domain-Specific Word Embedding and Hate Concepts in Hate Speech Detection2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10650500(1-8)Online publication date: 30-Jun-2024
https://doi.org/10.1109/IJCNN60899.2024.10650500
Abburi HParikh PChhaya NVarma V(2024)Multi-task learning neural framework for categorizing sexismComputer Speech and Language10.1016/j.csl.2023.10153583:COnline publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1016/j.csl.2023.101535
Tian LHuang NZhang X(2024)Large Language Model Cascades and Persona-Based In-Context Learning for Multilingual Sexism DetectionExperimental IR Meets Multilinguality, Multimodality, and Interaction10.1007/978-3-031-71736-9_18(254-265)Online publication date: 9-Sep-2024
https://dl.acm.org/doi/10.1007/978-3-031-71736-9_18
Sultan DToktarova AZhumadillayeva AAldeshov SMussiraliyeva SBeissenova GTursynbayev ABaenova GImanbayeva A(2023)Cyberbullying-related Hate Speech Detection Using Shallow-to-deep LearningComputers, Materials & Continua10.32604/cmc.2023.03299374:1(2115-2131)Online publication date: 2023
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Deepti JSayeedi MShoikot AIslam SRodela AZaman A(2023)Fem-Scale: A Data-Driven Approach for Quantifying Degree of Individual Feminism Perspective2023 IEEE International Conference on Contemporary Computing and Communications (InC4)10.1109/InC457730.2023.10263255(1-6)Online publication date: 21-Apr-2023
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Tri Wibowo BNurjanah DNurrahmi H(2023)Identification of Misogyny on Social Media in Indonesian Using Bidirectional Encoder Representations From Transformers (BERT)2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)10.1109/ICAIIC57133.2023.10067106(401-406)Online publication date: 20-Feb-2023
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