@inproceedings{hede-etal-2021-toxicity,
title = "From Toxicity in Online Comments to Incivility in {A}merican News: Proceed with Caution",
author = "Hede, Anushree and
Agarwal, Oshin and
Lu, Linda and
Mutz, Diana C. and
Nenkova, Ani",
editor = "Merlo, Paola and
Tiedemann, Jorg and
Tsarfaty, Reut",
booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume",
month = apr,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.eacl-main.225",
doi = "10.18653/v1/2021.eacl-main.225",
pages = "2620--2630",
abstract = "The ability to quantify incivility online, in news and in congressional debates is of great interest to political scientists. Computational tools for detecting online incivility for English are now fairly accessible and potentially could be applied more broadly. We test the Jigsaw Perspective API for its ability to detect the degree of incivility on a corpus that we developed, consisting of manual annotations of civility in American news. We demonstrate that toxicity models, as exemplified by Perspective, are inadequate for the analysis of incivility in news. We carry out error analysis that points to the need to develop methods to remove spurious correlations between words often mentioned in the news, especially identity descriptors and incivility. Without such improvements, applying Perspective or similar models on news is likely to lead to wrong conclusions, that are not aligned with the human perception of incivility.",
}
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<abstract>The ability to quantify incivility online, in news and in congressional debates is of great interest to political scientists. Computational tools for detecting online incivility for English are now fairly accessible and potentially could be applied more broadly. We test the Jigsaw Perspective API for its ability to detect the degree of incivility on a corpus that we developed, consisting of manual annotations of civility in American news. We demonstrate that toxicity models, as exemplified by Perspective, are inadequate for the analysis of incivility in news. We carry out error analysis that points to the need to develop methods to remove spurious correlations between words often mentioned in the news, especially identity descriptors and incivility. Without such improvements, applying Perspective or similar models on news is likely to lead to wrong conclusions, that are not aligned with the human perception of incivility.</abstract>
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%0 Conference Proceedings
%T From Toxicity in Online Comments to Incivility in American News: Proceed with Caution
%A Hede, Anushree
%A Agarwal, Oshin
%A Lu, Linda
%A Mutz, Diana C.
%A Nenkova, Ani
%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 hede-etal-2021-toxicity
%X The ability to quantify incivility online, in news and in congressional debates is of great interest to political scientists. Computational tools for detecting online incivility for English are now fairly accessible and potentially could be applied more broadly. We test the Jigsaw Perspective API for its ability to detect the degree of incivility on a corpus that we developed, consisting of manual annotations of civility in American news. We demonstrate that toxicity models, as exemplified by Perspective, are inadequate for the analysis of incivility in news. We carry out error analysis that points to the need to develop methods to remove spurious correlations between words often mentioned in the news, especially identity descriptors and incivility. Without such improvements, applying Perspective or similar models on news is likely to lead to wrong conclusions, that are not aligned with the human perception of incivility.
%R 10.18653/v1/2021.eacl-main.225
%U https://aclanthology.org/2021.eacl-main.225
%U https://doi.org/10.18653/v1/2021.eacl-main.225
%P 2620-2630
Markdown (Informal)
[From Toxicity in Online Comments to Incivility in American News: Proceed with Caution](https://aclanthology.org/2021.eacl-main.225) (Hede et al., EACL 2021)
ACL