# Not All Comments are Equal: Insights into Comment Moderation from a Topic-Aware Model

arXiv: https://arxiv.org/abs/2109.10033 · HTML: https://mlsanity.com/paper/2109.10033/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 29 references that are in the map.
- First posted on arXiv 21 Sep 2021.

## Builds on (most cited first, 13 of 13)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Topic Modeling in Embedding Spaces](https://mlsanity.com/paper/1907.04907/) (2019)
- [Directions in Abusive Language Training Data: Garbage In, Garbage Out](https://mlsanity.com/paper/2004.01670/) (2020)
- [Neural Variational Inference for Text Processing](https://mlsanity.com/paper/1511.06038/) (2015)
- [Detecting Online Hate Speech Using Context Aware Models](https://mlsanity.com/paper/1710.07395/) (2017)
- [TopicRNN: A Recurrent Neural Network with Long-Range Semantic Dependency](https://mlsanity.com/paper/1611.01702/) (2016)
- [HABERTOR: An Efficient and Effective Deep Hatespeech Detector](https://mlsanity.com/paper/2010.08865/) (2020)
- [Automatic Labelling of Topics with Neural Embeddings](https://mlsanity.com/paper/1612.05340/) (2016)
- [Topic Compositional Neural Language Model](https://mlsanity.com/paper/1712.09783/) (2017)
- [Improved Abusive Comment Moderation with User Embeddings](https://mlsanity.com/paper/1708.03699/) (2017)
- [Automatic Generation of Topic Labels](https://mlsanity.com/paper/2006.00127/) (2020)
- [Improving Moderation of Online Discussions via Interpretable Neural Models](https://mlsanity.com/paper/1809.06906/) (2018)
- [Explainable and Discourse Topic-aware Neural Language Understanding](https://mlsanity.com/paper/2006.10632/) (2020)

## Built on by (most cited first, 2 of 2)

- [Dimensions of Online Conflict: Towards Modeling Agonism](https://mlsanity.com/paper/2311.03584/) (2023)
- [Leveraging Large Language Models and Topic Modeling for Toxicity Classification](https://mlsanity.com/paper/2411.17876/) (2024)

## Abstract (arXiv)

Moderation of reader comments is a significant problem for online news platforms. Here, we experiment with models for automatic moderation, using a dataset of comments from a popular Croatian newspaper. Our analysis shows that while comments that violate the moderation rules mostly share common linguistic and thematic features, their content varies across the different sections of the newspaper. We therefore make our models topic-aware, incorporating semantic features from a topic model into the classification decision. Our results show that topic information improves the performance of the model, increases its confidence in correct outputs, and helps us understand the model's outputs.