Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

4-2020

Abstract

Understanding the causes or triggers of toxicity adds a new dimension to the prevention of toxic behavior in online discussions. In this research, we define toxicity triggers in online discussions as a non-toxic comment that lead to toxic replies. Then, we build a neural network-based prediction model for toxicity trigger. The prediction model incorporates text-based features and derived features from previous studies that pertain to shifts in sentiment, topic flow, and discussion context. Our findings show that triggers of toxicity contain identifiable features and that incorporating shift features with the discussion context can be detected with a ROC-AUC score of 0.87. We discuss implications for online communities and also possible further analysis of online toxicity and its root causes.

Keywords

Neural networks, Online discussion, Reddit, Toxicity, Trigger detection

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing | Social Media

Research Areas

Data Science and Engineering

Publication

WWW '20: Proceedings of The Web Conference 2020, Taipei, Taiwan, April 20-24

First Page

3033

Last Page

3040

ISBN

9781450370233

Identifier

10.1145/3366423.3380074

Publisher

ACM

City or Country

New York

Copyright Owner and License

Publisher

Additional URL

https://doi.org/10.1145/3366423.3380074

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