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        <title>#NotAWhore! A Computational Linguistic Perspective of Rape Culture and Victimization on Social Media</title>
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        <namePart type="given">Ashima</namePart>
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            <title>Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop</title>
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        <name type="personal">
            <namePart type="given">Shruti</namePart>
            <namePart type="family">Rijhwani</namePart>
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            <namePart type="given">Jiangming</namePart>
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    <abstract>The recent surge in online forums and movements supporting sexual assault survivors has led to the emergence of a ‘virtual bubble’ where survivors can recount their stories. However, this also makes the survivors vulnerable to bullying, trolling and victim blaming. Specifically, victim blaming has been shown to have acute psychological effects on the survivors and further discourage formal reporting of such crimes. Therefore, it is important to devise computationally relevant methods to identify and prevent victim blaming to protect the victims. In our work, we discuss the drastic effects of victim blaming through a short case study and then propose a single step transfer-learning based classification method to identify victim blaming language on Twitter. Finally, we compare the performance of our proposed model against various deep learning and machine learning models on a manually annotated domain-specific dataset.</abstract>
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%0 Conference Proceedings
%T #NotAWhore! A Computational Linguistic Perspective of Rape Culture and Victimization on Social Media
%A Suvarna, Ashima
%A Bhalla, Grusha
%Y Rijhwani, Shruti
%Y Liu, Jiangming
%Y Wang, Yizhong
%Y Dror, Rotem
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F suvarna-bhalla-2020-notawhore
%X The recent surge in online forums and movements supporting sexual assault survivors has led to the emergence of a ‘virtual bubble’ where survivors can recount their stories. However, this also makes the survivors vulnerable to bullying, trolling and victim blaming. Specifically, victim blaming has been shown to have acute psychological effects on the survivors and further discourage formal reporting of such crimes. Therefore, it is important to devise computationally relevant methods to identify and prevent victim blaming to protect the victims. In our work, we discuss the drastic effects of victim blaming through a short case study and then propose a single step transfer-learning based classification method to identify victim blaming language on Twitter. Finally, we compare the performance of our proposed model against various deep learning and machine learning models on a manually annotated domain-specific dataset.
%R 10.18653/v1/2020.acl-srw.43
%U https://aclanthology.org/2020.acl-srw.43
%U https://doi.org/10.18653/v1/2020.acl-srw.43
%P 328-335

Markdown (Informal)

[#NotAWhore! A Computational Linguistic Perspective of Rape Culture and Victimization on Social Media](https://aclanthology.org/2020.acl-srw.43) (Suvarna & Bhalla, ACL 2020)

• #NotAWhore! A Computational Linguistic Perspective of Rape Culture and Victimization on Social Media (Suvarna & Bhalla, ACL 2020)

ACL

• Ashima Suvarna and Grusha Bhalla. 2020. #NotAWhore! A Computational Linguistic Perspective of Rape Culture and Victimization on Social Media. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, pages 328–335, Online. Association for Computational Linguistics.