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Cited by 11Hate Speech and Cyberbullying DetectionEthics and Social Impacts of AISentiment Analysis and Opinion Mining

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Misogynoir: public online response towards self-reported misogynoir

Kwarteng, Joseph, Perfumi, Serena Coppolino, Farrell, Tracie, Fernandez, Miriam·2021·Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
DOI 10.1145/3487351.3488342
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Abstract

"Misogynoir" refers to the specific forms of misogyny that Black women experience, which couple racism and sexism together. To better understand the online manifestations of this type of hate, and to propose methods that can automatically identify it, in this paper, we conduct a study on 4 cases of Black women in Tech reporting experiences of misogynoir on the Twitter platform. We follow the reactions to these cases (both supportive and non-supportive responses), and categorise them within a model of misogynoir that highlights experiences of Tone Policing, White Centring, Racial Gaslighting and Defensiveness. As an intersectional form of abusive or hateful speech, we investigate the possibilities and challenges to detect online instances of misogynoir in an automated way. We then conduct a closer qualitative analysis on messages of support and non-support to look at some of these categories in more detail. The purpose of this investigation is to understand responses to misogynoir online, including doubling down on misogynoir, engaging in performative allyship, and showing solidarity with Black women in tech.

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Research works printed but not linked: 22

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Cited by

Sorted by citations

  • Misogynoir: challenges in detecting intersectional hate

    Kwarteng, J., Perfumi, S. C., Farrell, T., Third, A., & Fernandez, M. (2022) · Social Network Analysis and Mining · doi:10.1007/s13278-022-00993-7 · Cited by 30

    “This study expands on our previous work (Kwarteng et al. 2021) that analysed the public response in Twitter towards the self-reported experiences of misogynoir of four Black women (case studies) in tech.”
    BackgroundMethodResultInfluentialSelf-citation

See all 11

Metadata

Authors
Kwarteng, J., Perfumi, S. C., Farrell, T., & Fernandez, M.
Affiliations
  • The Open University (GB)
  • Stockholm University (SE)
  • Stockholm University, Sweden
ORCID
Venue
Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Publisher
ACM
Published
2021-11-08
Pages
228-235
DOI
10.1145/3487351.3488342
OpenAlex
W4213385622
Cited by
11
References
2
Citations by year
2026: 3 · 2025: 1 · 2024: 4 · 2023: 2 · 2022: 1
FWCI
0.81
Type
conference paper
Language
en
Open access
closed
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations
IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
  • Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
  • Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
Keywords
misogynoir, online harassment, Twitter, defensiveness, performative allyship, intersectionality

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