Are These Comments Triggering? Predicting Triggers of Toxicity in Online Discussions



: Hind Almerekhi, Haewoon Kwak, Joni Salminen, Bernard J. Jansen

: Yennun Huang, Irwin King, Tie-Yan Liu, Maarten van Steen

: International World Wide Web Conference

Publisher: Association for Computing Machinery

: 2020

: The Web Conference 2020: Proceedings of The World Wide Web Conference WWW 2020

: 3033

: 3040

: 978-1-4503-7023-3

DOI: https://doi.org/10.1145/3366423.3380074

: https://doi.org/10.1145/3366423.3380074

: https://research.utu.fi/converis/portal/detail/Publication/50745995


Understanding the causes or triggers of toxicity adds a new dimension to the prevention of toxic behavior in online discussions. In this research, we define toxicity triggers in online discussions as a non-toxic comment that lead to toxic replies. Then, we build a neural network-based prediction model for toxicity trigger. The prediction model incorporates text-based features and derived features from previous studies that pertain to shifts in sentiment, topic flow, and discussion context. Our findings show that triggers of toxicity contain identifiable features and that incorporating shift features with the discussion context can be detected with a ROC-AUC score of 0.87. We discuss implications for online communities and also possible further analysis of online toxicity and its root causes.C1 - Taipei, TaiwanC3 - Proceedings of The Web Conference 2020

Last updated on 2024-26-11 at 21:23