TY - GEN
T1 - Detecting toxicity triggers in online discussions
AU - Almerekhi, Hind
AU - Jansen, Bernard J.
AU - Kwak, Haewoon
AU - Salminen, Joni
PY - 2019/9/12
Y1 - 2019/9/12
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85073372109
UR - https://www.scopus.com/inward/citedby.url?scp=85073372109&partnerID=8YFLogxK
U2 - 10.1145/3342220.3344933
DO - 10.1145/3342220.3344933
M3 - Conference contribution
T3 - HT 2019 - Proceedings of the 30th ACM Conference on Hypertext and Social Media
SP - 291
EP - 292
BT - HT 2019 - Proceedings of the 30th ACM Conference on Hypertext and Social Media
PB - Association for Computing Machinery, Inc
T2 - 30th ACM Conference on Hypertext and Social Media, HT 2019
Y2 - 17 September 2019 through 20 September 2019
ER -