Incivility detection in online comments

Farig Sadeque, Stephen Rains, Yotam Shmargad, Kate Kenski, Kevin Coe, Steven Bethard

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

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 name-calling 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.

Original languageEnglish (US)
Title of host publication*SEM@NAACL-HLT 2019 - 8th Joint Conference on Lexical and Computational Semantics
PublisherAssociation for Computational Linguistics (ACL)
Pages283-291
Number of pages9
ISBN (Electronic)9781948087933
StatePublished - 2019
Event8th Joint Conference on Lexical and Computational Semantics, *SEM@NAACL-HLT 2019 - Minneapolis, United States
Duration: Jun 6 2019Jun 7 2019

Publication series

Name*SEM@NAACL-HLT 2019 - 8th Joint Conference on Lexical and Computational Semantics

Conference

Conference8th Joint Conference on Lexical and Computational Semantics, *SEM@NAACL-HLT 2019
Country/TerritoryUnited States
CityMinneapolis
Period6/6/196/7/19

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

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