%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