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            <title>Proceedings of the Third Workshop on Abusive Language Online</title>
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    <abstract>Discussion forum participation represents one of the crucial factors for learning and often the only way of supporting social interactions in online settings. However, as much as sharing new ideas or asking thoughtful questions contributes learning, verbally abusive behaviors, such as expressing negative emotions in online discussions, could have disproportionate detrimental effects. To provide means for mitigating the potential negative effects on course participation and learning, we developed an automated classifier for identifying communication that show linguistic patterns associated with hostility in online forums. In so doing, we employ several well-established automated text analysis tools and build on the common practices for handling highly imbalanced datasets and reducing the sensitivity to overfitting. Although still in its infancy, our approach shows promising results (ROC AUC .73) towards establishing a robust detector of abusive behaviors. We further provide an overview of the classification (linguistic and contextual) features most indicative of online aggression.</abstract>
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    <identifier type="doi">10.18653/v1/W19-3505</identifier>
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%0 Conference Proceedings
%T Automated Identification of Verbally Abusive Behaviors in Online Discussions
%A Joksimovic, Srecko
%A Baker, Ryan S.
%A Ocumpaugh, Jaclyn
%A Andres, Juan Miguel L.
%A Tot, Ivan
%A Wang, Elle Yuan
%A Dawson, Shane
%Y Roberts, Sarah T.
%Y Tetreault, Joel
%Y Prabhakaran, Vinodkumar
%Y Waseem, Zeerak
%S Proceedings of the Third Workshop on Abusive Language Online
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F joksimovic-etal-2019-automated
%X Discussion forum participation represents one of the crucial factors for learning and often the only way of supporting social interactions in online settings. However, as much as sharing new ideas or asking thoughtful questions contributes learning, verbally abusive behaviors, such as expressing negative emotions in online discussions, could have disproportionate detrimental effects. To provide means for mitigating the potential negative effects on course participation and learning, we developed an automated classifier for identifying communication that show linguistic patterns associated with hostility in online forums. In so doing, we employ several well-established automated text analysis tools and build on the common practices for handling highly imbalanced datasets and reducing the sensitivity to overfitting. Although still in its infancy, our approach shows promising results (ROC AUC .73) towards establishing a robust detector of abusive behaviors. We further provide an overview of the classification (linguistic and contextual) features most indicative of online aggression.
%R 10.18653/v1/W19-3505
%U https://aclanthology.org/W19-3505
%U https://doi.org/10.18653/v1/W19-3505
%P 36-45

Markdown (Informal)

[Automated Identification of Verbally Abusive Behaviors in Online Discussions](https://aclanthology.org/W19-3505) (Joksimovic et al., ALW 2019)

• Automated Identification of Verbally Abusive Behaviors in Online Discussions (Joksimovic et al., ALW 2019)

ACL

• Srecko Joksimovic, Ryan S. Baker, Jaclyn Ocumpaugh, Juan Miguel L. Andres, Ivan Tot, Elle Yuan Wang, and Shane Dawson. 2019. Automated Identification of Verbally Abusive Behaviors in Online Discussions. In Proceedings of the Third Workshop on Abusive Language Online, pages 36–45, Florence, Italy. Association for Computational Linguistics.