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    <abstract>We examine the task of detecting implicitly abusive comparisons (e.g. “Your hair looks like you have been electrocuted”). Implicitly abusive comparisons are abusive comparisons in which abusive words (e.g. “dumbass” or “scum”) are absent. We detail the process of creating a novel dataset for this task via crowdsourcing that includes several measures to obtain a sufficiently representative and unbiased set of comparisons. We also present classification experiments that include a range of linguistic features that help us better understand the mechanisms underlying abusive comparisons.</abstract>
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%0 Conference Proceedings
%T Implicitly Abusive Comparisons – A New Dataset and Linguistic Analysis
%A Wiegand, Michael
%A Geulig, Maja
%A Ruppenhofer, Josef
%Y Merlo, Paola
%Y Tiedemann, Jorg
%Y Tsarfaty, Reut
%S Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
%D 2021
%8 April
%I Association for Computational Linguistics
%C Online
%F wiegand-etal-2021-implicitly
%X We examine the task of detecting implicitly abusive comparisons (e.g. “Your hair looks like you have been electrocuted”). Implicitly abusive comparisons are abusive comparisons in which abusive words (e.g. “dumbass” or “scum”) are absent. We detail the process of creating a novel dataset for this task via crowdsourcing that includes several measures to obtain a sufficiently representative and unbiased set of comparisons. We also present classification experiments that include a range of linguistic features that help us better understand the mechanisms underlying abusive comparisons.
%R 10.18653/v1/2021.eacl-main.27
%U https://aclanthology.org/2021.eacl-main.27/
%U https://doi.org/10.18653/v1/2021.eacl-main.27
%P 358-368

Markdown (Informal)

[Implicitly Abusive Comparisons – A New Dataset and Linguistic Analysis](https://aclanthology.org/2021.eacl-main.27/) (Wiegand et al., EACL 2021)

• Implicitly Abusive Comparisons – A New Dataset and Linguistic Analysis (Wiegand et al., EACL 2021)

ACL

• Michael Wiegand, Maja Geulig, and Josef Ruppenhofer. 2021. Implicitly Abusive Comparisons – A New Dataset and Linguistic Analysis. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 358–368, Online. Association for Computational Linguistics.