Detecting toxicity triggers in online discussions

• Main Authors: Hamad Bin Khalifa University, KWAK, Haewoon
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2019
• Subjects: Neural networks; Reddit; Social media; Toxicity; Trigger detection; Theory and Algorithms
• Online Access: https://ink.library.smu.edu.sg/sis_research/5656; https://ink.library.smu.edu.sg/context/sis_research/article/6659/viewcontent/3342220.3344933.pdf
• Institution: Singapore Management University

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.