@inproceedings{ed30d3c751414c3795db56a809d00568,
title = "Detecting toxicity triggers in online discussions",
abstract = "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.",
keywords = "Neural networks, Reddit, Social media, Toxicity, Trigger detection",
author = "Hind Almerekhi and Jansen, {Bernard J.} and Haewoon Kwak and Joni Salminen",
note = "Publisher Copyright: {\textcopyright} 2019 Copyright held by the owner/author(s).; 30th ACM Conference on Hypertext and Social Media, HT 2019 ; Conference date: 17-09-2019 Through 20-09-2019",
year = "2019",
month = sep,
day = "12",
doi = "10.1145/3342220.3344933",
language = "English",
series = "HT 2019 - Proceedings of the 30th ACM Conference on Hypertext and Social Media",
publisher = "Association for Computing Machinery, Inc",
pages = "291--292",
booktitle = "HT 2019 - Proceedings of the 30th ACM Conference on Hypertext and Social Media",
}