Publication | Open Access
Detecting Toxicity Triggers in Online Discussions
43
Citations
4
References
2019
Year
Unknown Venue
Abuse DetectionEngineeringCommunicationMultimodal Sentiment AnalysisCorpus LinguisticsJournalismText MiningNatural Language ProcessingComputational Social ScienceSocial MediaToxicity TriggersData ScienceAffective ComputingToxicologyConversation AnalysisContent AnalysisClinical ToxicologySentiment ShiftSocial Medium MiningToxic CommentsOnline DiscussionsPredictive ToxicologyForensic ToxicologySocial Medium DataArts
Despite the considerable interest in the detection of toxic comments, there has been little research investigating the causes -- i.e., triggers -- of toxicity. In this work, we first propose a formal definition of triggers of toxicity in online communities. We proceed to build an LSTM neural network model using textual features of comments, and then, based on a comprehensive review of previous literature, we incorporate topical and sentiment shift in interactions as features. Our model achieves an average accuracy of 82.5% of detecting toxicity triggers from diverse Reddit communities.
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