# Attending the Emotions to Detect Online Abusive Language

arXiv: https://arxiv.org/abs/1909.03100 · HTML: https://mlsanity.com/paper/1909.03100/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Sep 2019.

## Built on by (most cited first, 7 of 7)

- [Hatemoji: A Test Suite and Adversarially-Generated Dataset for Benchmarking and Detecting Emoji-based Hate](https://mlsanity.com/paper/2108.05921/) (2021)
- [Positive and Risky Message Assessment for Music Products](https://mlsanity.com/paper/2309.10182/) (2023)
- [Modeling Users and Online Communities for Abuse Detection: A Position on Ethics and Explainability](https://mlsanity.com/paper/2103.17191/) (2021)
- [A Case Study of Deep Learning Based Multi-Modal Methods for Predicting the Age-Suitability Rating of Movie Trailers](https://mlsanity.com/paper/2101.11704/) (2021)
- [Improving Cross-Domain Hate Speech Generalizability with Emotion Knowledge](https://mlsanity.com/paper/2311.14865/) (2023)
- [Topic Bias in Emotion Classification](https://mlsanity.com/paper/2312.09043/) (2023)
- [Hierarchical Sentiment Analysis Framework for Hate Speech Detection: Implementing Binary and Multiclass Classification Strategy](https://mlsanity.com/paper/2411.05819/) (2024)

## Abstract (arXiv)

In recent years, abusive behavior has become a serious issue in online social networks. In this paper, we present a new corpus from a semi-anonymous social media platform, which contains the instances of offensive and neutral classes. We introduce a single deep neural architecture that considers both local and sequential information from the text in order to detect abusive language. Along with this model, we introduce a new attention mechanism called emotion-aware attention. This mechanism utilizes the emotions behind the text to find the most important words within that text. We experiment with this model on our dataset and later present the analysis. Additionally, we evaluate our proposed method on different corpora and show new state-of-the-art results with respect to offensive language detection.