# Convolutional Neural Networks for Toxic Comment Classification

arXiv: https://arxiv.org/abs/1802.09957 · HTML: https://mlsanity.com/paper/1802.09957/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Feb 2018.

## Built on by (most cited first, 24 of 24)

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- [Personalized Prediction of Offensive News Comments by Considering the Characteristics of Commenters](https://mlsanity.com/paper/2212.13205/) (2022)
- [Classification of social media Toxic comments using Machine learning models](https://mlsanity.com/paper/2304.06934/) (2023)
- [AI Enabled User-Specific Cyberbullying Severity Detection with Explainability](https://mlsanity.com/paper/2503.10650/) (2025)
- [Beyond One-Size-Fits-All: Personalized Harmful Content Detection with In-Context Learning](https://mlsanity.com/paper/2511.05532/) (2025)
- [When Youth Enter the Algorithmic Wild: Discovering and Understanding Potentially Harmful Teen Videos on Douyin and Kwai](https://mlsanity.com/paper/2605.23598/) (2026)

## Abstract (arXiv)

Flood of information is produced in a daily basis through the global Internet usage arising from the on-line interactive communications among users. While this situation contributes significantly to the quality of human life, unfortunately it involves enormous dangers, since on-line texts with high toxicity can cause personal attacks, on-line harassment and bullying behaviors. This has triggered both industrial and research community in the last few years while there are several tries to identify an efficient model for on-line toxic comment prediction. However, these steps are still in their infancy and new approaches and frameworks are required. On parallel, the data explosion that appears constantly, makes the construction of new machine learning computational tools for managing this information, an imperative need. Thankfully advances in hardware, cloud computing and big data management allow the development of Deep Learning approaches appearing very promising performance so far. For text classification in particular the use of Convolutional Neural Networks (CNN) have recently been proposed approaching text analytics in a modern manner emphasizing in the structure of words in a document. In this work, we employ this approach to discover toxic comments in a large pool of documents provided by a current Kaggle's competition regarding Wikipedia's talk page edits. To justify this decision we choose to compare CNNs against the traditional bag-of-words approach for text analysis combined with a selection of algorithms proven to be very effective in text classification. The reported results provide enough evidence that CNN enhance toxic comment classification reinforcing research interest towards this direction.