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The challenge of automatic detection of toxic comments online has been thesubject of a lot of research recently, but the focus has been mostly ondetecting it in individual messages after they have been posted. Some authorshave tried to predict if a conversation will derail into toxicity using thefeatures of the first few messages. In this paper, we combine that approachwith previous work on toxicity detection using sentiment information, and showhow the sentiments expressed in the first messages of a conversation can helppredict upcoming toxicity. Our results show that adding sentiment features doeshelp improve the accuracy of toxicity prediction, and also allow us to makeimportant observations on the general task of preemptive toxicity detection.

Using Sentiment Information for Preemptive Detection of Toxic Comments in Online Conversations