@inproceedings{sadeque-etal-2019-incivility,
title = "Incivility Detection in Online Comments",
author = "Sadeque, Farig and
Rains, Stephen and
Shmargad, Yotam and
Kenski, Kate and
Coe, Kevin and
Bethard, Steven",
editor = "Mihalcea, Rada and
Shutova, Ekaterina and
Ku, Lun-Wei and
Evang, Kilian and
Poria, Soujanya",
booktitle = "Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*{SEM} 2019)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S19-1031",
doi = "10.18653/v1/S19-1031",
pages = "283--291",
abstract = "Incivility in public discourse has been a major concern in recent times as it can affect the quality and tenacity of the discourse negatively. In this paper, we present neural models that can learn to detect name-calling and vulgarity from a newspaper comment section. We show that in contrast to prior work on detecting toxic language, fine-grained incivilities like namecalling cannot be accurately detected by simple models like logistic regression. We apply the models trained on the newspaper comments data to detect uncivil comments in a Russian troll dataset, and find that despite the change of domain, the model makes accurate predictions.",
}
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<abstract>Incivility in public discourse has been a major concern in recent times as it can affect the quality and tenacity of the discourse negatively. In this paper, we present neural models that can learn to detect name-calling and vulgarity from a newspaper comment section. We show that in contrast to prior work on detecting toxic language, fine-grained incivilities like namecalling cannot be accurately detected by simple models like logistic regression. We apply the models trained on the newspaper comments data to detect uncivil comments in a Russian troll dataset, and find that despite the change of domain, the model makes accurate predictions.</abstract>
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%0 Conference Proceedings
%T Incivility Detection in Online Comments
%A Sadeque, Farig
%A Rains, Stephen
%A Shmargad, Yotam
%A Kenski, Kate
%A Coe, Kevin
%A Bethard, Steven
%Y Mihalcea, Rada
%Y Shutova, Ekaterina
%Y Ku, Lun-Wei
%Y Evang, Kilian
%Y Poria, Soujanya
%S Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F sadeque-etal-2019-incivility
%X Incivility in public discourse has been a major concern in recent times as it can affect the quality and tenacity of the discourse negatively. In this paper, we present neural models that can learn to detect name-calling and vulgarity from a newspaper comment section. We show that in contrast to prior work on detecting toxic language, fine-grained incivilities like namecalling cannot be accurately detected by simple models like logistic regression. We apply the models trained on the newspaper comments data to detect uncivil comments in a Russian troll dataset, and find that despite the change of domain, the model makes accurate predictions.
%R 10.18653/v1/S19-1031
%U https://aclanthology.org/S19-1031
%U https://doi.org/10.18653/v1/S19-1031
%P 283-291
Markdown (Informal)
[Incivility Detection in Online Comments](https://aclanthology.org/S19-1031) (Sadeque et al., *SEM 2019)
ACL
- Farig Sadeque, Stephen Rains, Yotam Shmargad, Kate Kenski, Kevin Coe, and Steven Bethard. 2019. Incivility Detection in Online Comments. In Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019), pages 283–291, Minneapolis, Minnesota. Association for Computational Linguistics.