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        <title>JudithJeyafreeda at SemEval-2023 Task 10: Machine Learning for Explainable Detection of Online Sexism</title>
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        <namePart type="given">Judith</namePart>
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            <namePart type="given">Harish</namePart>
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            <namePart type="given">Ritesh</namePart>
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            <namePart type="given">Elisa</namePart>
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    <abstract>The rise of the internet and social media platforms has brought about significant changes in how people interact with each another. For a lot of people, the internet have also become the only source of news and information about the world. Thus due to the increase in accessibility of information, online sexism has also increased. Efforts should be made to make the internet a safe space for everyone, irrespective of gender, both from a larger social norms perspective and legal or technical regulations to help alleviate online gender-based violence. As a part of this, this paper explores simple methods that can be easily deployed to automatically detect online sexism in textual statements.</abstract>
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%0 Conference Proceedings
%T JudithJeyafreeda at SemEval-2023 Task 10: Machine Learning for Explainable Detection of Online Sexism
%A Andrew, Judith Jeyafreeda
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Da San Martino, Giovanni
%Y Tayyar Madabushi, Harish
%Y Kumar, Ritesh
%Y Sartori, Elisa
%S Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F andrew-2023-judithjeyafreeda
%X The rise of the internet and social media platforms has brought about significant changes in how people interact with each another. For a lot of people, the internet have also become the only source of news and information about the world. Thus due to the increase in accessibility of information, online sexism has also increased. Efforts should be made to make the internet a safe space for everyone, irrespective of gender, both from a larger social norms perspective and legal or technical regulations to help alleviate online gender-based violence. As a part of this, this paper explores simple methods that can be easily deployed to automatically detect online sexism in textual statements.
%R 10.18653/v1/2023.semeval-1.184
%U https://aclanthology.org/2023.semeval-1.184
%U https://doi.org/10.18653/v1/2023.semeval-1.184
%P 1324-1328

Markdown (Informal)

[JudithJeyafreeda at SemEval-2023 Task 10: Machine Learning for Explainable Detection of Online Sexism](https://aclanthology.org/2023.semeval-1.184) (Andrew, SemEval 2023)

• JudithJeyafreeda at SemEval-2023 Task 10: Machine Learning for Explainable Detection of Online Sexism (Andrew, SemEval 2023)

ACL

• Judith Jeyafreeda Andrew. 2023. JudithJeyafreeda at SemEval-2023 Task 10: Machine Learning for Explainable Detection of Online Sexism. In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 1324–1328, Toronto, Canada. Association for Computational Linguistics.