Improving Moderation of Online Discussions via Interpretable Neural Models
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Abstract (arXiv)
Growing amount of comments make online discussions difficult to moderate by human moderators only. Antisocial behavior is a common occurrence that often discourages other users from participating in discussion. We propose a neural network based method that partially automates the moderation process. It consists of two steps. First, we detect inappropriate comments for moderators to see. Second, we highlight inappropriate parts within these comments to make the moderation faster. We evaluated our method on data from a major Slovak news discussion platform.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Sep 2018.
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Citing papers per year: 2021: 3; 2022: 1.
4 papers.
- 2021Countering Online Hate Speech: An NLP Perspectivecited by 12
- 2022Efficient, Uncertainty-based Moderation of Neural Networks Text Classifierscited by 7
- 2021Not All Comments are Equal: Insights into Comment Moderation from a Topic-Aware Modelcited by 2
- 2021Addressing Hate Speech with Data Science: An Overview from Computer Science Perspectivecited by 1
- 2022Efficient, Uncertainty-based Moderation of Neural Networks Text Classifiers4 Apr 2022
- 2021Not All Comments are Equal: Insights into Comment Moderation from a Topic-Aware Model21 Sep 2021
- 2021Countering Online Hate Speech: An NLP Perspective7 Sep 2021
- 2021Addressing Hate Speech with Data Science: An Overview from Computer Science Perspective18 Mar 2021