@inproceedings{cascione-etal-2024-womens,
title = "Women`s Professions and Targeted Misogyny Online",
author = "Cascione, Alessio and
Cerulli, Aldo and
Marchiori Manerba, Marta and
Passaro, Lucia",
editor = "Dell'Orletta, Felice and
Lenci, Alessandro and
Montemagni, Simonetta and
Sprugnoli, Rachele",
booktitle = "Proceedings of the 10th Italian Conference on Computational Linguistics (CLiC-it 2024)",
month = dec,
year = "2024",
address = "Pisa, Italy",
publisher = "CEUR Workshop Proceedings",
url = "https://aclanthology.org/2024.clicit-1.22/",
pages = "182--189",
ISBN = "979-12-210-7060-6",
abstract = "With the increasing popularity of social media platforms, the dissemination of misogynistic content has become more prevalent and challenging to address. In this paper, we investigate the phenomenon of online misogyny on Twitter through the lens of hurtfulness, qualifying its different manifestation considering the profession of the targets of misogynistic attacks.By leveraging manual annotation and a BERTweet model trained for fine-grained misogyny identification, we find that specific types of misogynistic speech are more intensely directed towards particular professions: derailing discourse predominantly targets authors and cultural figures, while dominance-oriented speech and sexual harassment are mainly directed at politicians and athletes. Additionally, we use the HurtLex lexicon and ItEM to assign hurtfulness scores to tweets based on different hate speech categories. Our analysis reveals that these scores align with the profession-based distribution of misogynistic speech, highlighting the targeted nature of such attacks."
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<abstract>With the increasing popularity of social media platforms, the dissemination of misogynistic content has become more prevalent and challenging to address. In this paper, we investigate the phenomenon of online misogyny on Twitter through the lens of hurtfulness, qualifying its different manifestation considering the profession of the targets of misogynistic attacks.By leveraging manual annotation and a BERTweet model trained for fine-grained misogyny identification, we find that specific types of misogynistic speech are more intensely directed towards particular professions: derailing discourse predominantly targets authors and cultural figures, while dominance-oriented speech and sexual harassment are mainly directed at politicians and athletes. Additionally, we use the HurtLex lexicon and ItEM to assign hurtfulness scores to tweets based on different hate speech categories. Our analysis reveals that these scores align with the profession-based distribution of misogynistic speech, highlighting the targeted nature of such attacks.</abstract>
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%0 Conference Proceedings
%T Women‘s Professions and Targeted Misogyny Online
%A Cascione, Alessio
%A Cerulli, Aldo
%A Marchiori Manerba, Marta
%A Passaro, Lucia
%Y Dell’Orletta, Felice
%Y Lenci, Alessandro
%Y Montemagni, Simonetta
%Y Sprugnoli, Rachele
%S Proceedings of the 10th Italian Conference on Computational Linguistics (CLiC-it 2024)
%D 2024
%8 December
%I CEUR Workshop Proceedings
%C Pisa, Italy
%@ 979-12-210-7060-6
%F cascione-etal-2024-womens
%X With the increasing popularity of social media platforms, the dissemination of misogynistic content has become more prevalent and challenging to address. In this paper, we investigate the phenomenon of online misogyny on Twitter through the lens of hurtfulness, qualifying its different manifestation considering the profession of the targets of misogynistic attacks.By leveraging manual annotation and a BERTweet model trained for fine-grained misogyny identification, we find that specific types of misogynistic speech are more intensely directed towards particular professions: derailing discourse predominantly targets authors and cultural figures, while dominance-oriented speech and sexual harassment are mainly directed at politicians and athletes. Additionally, we use the HurtLex lexicon and ItEM to assign hurtfulness scores to tweets based on different hate speech categories. Our analysis reveals that these scores align with the profession-based distribution of misogynistic speech, highlighting the targeted nature of such attacks.
%U https://aclanthology.org/2024.clicit-1.22/
%P 182-189
Markdown (Informal)
[Women’s Professions and Targeted Misogyny Online](https://aclanthology.org/2024.clicit-1.22/) (Cascione et al., CLiC-it 2024)
ACL
- Alessio Cascione, Aldo Cerulli, Marta Marchiori Manerba, and Lucia Passaro. 2024. Women’s Professions and Targeted Misogyny Online. In Proceedings of the 10th Italian Conference on Computational Linguistics (CLiC-it 2024), pages 182–189, Pisa, Italy. CEUR Workshop Proceedings.