@inproceedings{shvets-etal-2021-targets,
title = "Targets and Aspects in Social Media Hate Speech",
author = "Shvets, Alexander and
Fortuna, Paula and
Soler, Juan and
Wanner, Leo",
editor = "Mostafazadeh Davani, Aida and
Kiela, Douwe and
Lambert, Mathias and
Vidgen, Bertie and
Prabhakaran, Vinodkumar and
Waseem, Zeerak",
booktitle = "Proceedings of the 5th Workshop on Online Abuse and Harms (WOAH 2021)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.woah-1.19",
doi = "10.18653/v1/2021.woah-1.19",
pages = "179--190",
abstract = "Mainstream research on hate speech focused so far predominantly on the task of classifying mainly social media posts with respect to predefined typologies of rather coarse-grained hate speech categories. This may be sufficient if the goal is to detect and delete abusive language posts. However, removal is not always possible due to the legislation of a country. Also, there is evidence that hate speech cannot be successfully combated by merely removing hate speech posts; they should be countered by education and counter-narratives. For this purpose, we need to identify (i) who is the target in a given hate speech post, and (ii) what aspects (or characteristics) of the target are attributed to the target in the post. As the first approximation, we propose to adapt a generic state-of-the-art concept extraction model to the hate speech domain. The outcome of the experiments is promising and can serve as inspiration for further work on the task",
}
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<abstract>Mainstream research on hate speech focused so far predominantly on the task of classifying mainly social media posts with respect to predefined typologies of rather coarse-grained hate speech categories. This may be sufficient if the goal is to detect and delete abusive language posts. However, removal is not always possible due to the legislation of a country. Also, there is evidence that hate speech cannot be successfully combated by merely removing hate speech posts; they should be countered by education and counter-narratives. For this purpose, we need to identify (i) who is the target in a given hate speech post, and (ii) what aspects (or characteristics) of the target are attributed to the target in the post. As the first approximation, we propose to adapt a generic state-of-the-art concept extraction model to the hate speech domain. The outcome of the experiments is promising and can serve as inspiration for further work on the task</abstract>
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%0 Conference Proceedings
%T Targets and Aspects in Social Media Hate Speech
%A Shvets, Alexander
%A Fortuna, Paula
%A Soler, Juan
%A Wanner, Leo
%Y Mostafazadeh Davani, Aida
%Y Kiela, Douwe
%Y Lambert, Mathias
%Y Vidgen, Bertie
%Y Prabhakaran, Vinodkumar
%Y Waseem, Zeerak
%S Proceedings of the 5th Workshop on Online Abuse and Harms (WOAH 2021)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F shvets-etal-2021-targets
%X Mainstream research on hate speech focused so far predominantly on the task of classifying mainly social media posts with respect to predefined typologies of rather coarse-grained hate speech categories. This may be sufficient if the goal is to detect and delete abusive language posts. However, removal is not always possible due to the legislation of a country. Also, there is evidence that hate speech cannot be successfully combated by merely removing hate speech posts; they should be countered by education and counter-narratives. For this purpose, we need to identify (i) who is the target in a given hate speech post, and (ii) what aspects (or characteristics) of the target are attributed to the target in the post. As the first approximation, we propose to adapt a generic state-of-the-art concept extraction model to the hate speech domain. The outcome of the experiments is promising and can serve as inspiration for further work on the task
%R 10.18653/v1/2021.woah-1.19
%U https://aclanthology.org/2021.woah-1.19
%U https://doi.org/10.18653/v1/2021.woah-1.19
%P 179-190
Markdown (Informal)
[Targets and Aspects in Social Media Hate Speech](https://aclanthology.org/2021.woah-1.19) (Shvets et al., WOAH 2021)
ACL
- Alexander Shvets, Paula Fortuna, Juan Soler, and Leo Wanner. 2021. Targets and Aspects in Social Media Hate Speech. In Proceedings of the 5th Workshop on Online Abuse and Harms (WOAH 2021), pages 179–190, Online. Association for Computational Linguistics.