# Improving Moderation of Online Discussions via Interpretable Neural Models

arXiv: https://arxiv.org/abs/1809.06906 · HTML: https://mlsanity.com/paper/1809.06906/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Sep 2018.

## Built on by (most cited first, 4 of 4)

- [Countering Online Hate Speech: An NLP Perspective](https://mlsanity.com/paper/2109.02941/) (2021)
- [Efficient, Uncertainty-based Moderation of Neural Networks Text Classifiers](https://mlsanity.com/paper/2204.01334/) (2022)
- [Not All Comments are Equal: Insights into Comment Moderation from a Topic-Aware Model](https://mlsanity.com/paper/2109.10033/) (2021)
- [Addressing Hate Speech with Data Science: An Overview from Computer Science Perspective](https://mlsanity.com/paper/2103.10489/) (2021)

## 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.