@inproceedings{5cd82e3c644047b5881d8d9c282f3735,
title = "Are These Comments Triggering? Predicting Triggers of Toxicity in Online Discussions",
abstract = "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.",
keywords = "Reddit, neural networks, online discussion, toxicity, trigger detection",
author = "Hind Almerekhi and Haewoon Kwak and Joni Salminen and Jansen, {Bernard J.}",
note = "Publisher Copyright: {\textcopyright} 2020 ACM.; 29th International World Wide Web Conference, WWW 2020 ; Conference date: 20-04-2020 Through 24-04-2020",
year = "2020",
month = apr,
day = "20",
doi = "10.1145/3366423.3380074",
language = "English",
series = "The Web Conference 2020 - Proceedings of the World Wide Web Conference, WWW 2020",
publisher = "Association for Computing Machinery, Inc",
pages = "3033--3040",
booktitle = "The Web Conference 2020 - Proceedings of the World Wide Web Conference, WWW 2020",
}