# Degree based Classification of Harmful Speech using Twitter Data

arXiv: https://arxiv.org/abs/1806.04197 · HTML: https://mlsanity.com/paper/1806.04197/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jun 2018.

## Built on by (most cited first, 8 of 8)

- [Towards generalisable hate speech detection: a review on obstacles and solutions](https://mlsanity.com/paper/2102.08886/) (2021)
- [Large-Scale Hate Speech Detection with Cross-Domain Transfer](https://mlsanity.com/paper/2203.01111/) (2022)
- [Leveraging Annotator Disagreement for Text Classification](https://mlsanity.com/paper/2409.17577/) (2024)
- [Hidden behind the obvious: misleading keywords and implicitly abusive language on social media](https://mlsanity.com/paper/2205.01374/) (2022)
- [Hate Speech Classification Using SVM and Naive BAYES](https://mlsanity.com/paper/2204.07057/) (2022)
- [Toxic language detection: a systematic review of Arabic datasets](https://mlsanity.com/paper/2312.07228/) (2023)
- [XGUARD: A Graded Benchmark for Evaluating Safety Failures of Large Language Models on Extremist Content](https://mlsanity.com/paper/2506.00973/) (2025)
- [Improving Hate Speech Classification with Cross-Taxonomy Dataset Integration](https://mlsanity.com/paper/2503.05357/) (2025)

## Abstract (arXiv)

Harmful speech has various forms and it has been plaguing the social media in different ways. If we need to crackdown different degrees of hate speech and abusive behavior amongst it, the classification needs to be based on complex ramifications which needs to be defined and hold accountable for, other than racist, sexist or against some particular group and community. This paper primarily describes how we created an ontological classification of harmful speech based on degree of hateful intent, and used it to annotate twitter data accordingly. The key contribution of this paper is the new dataset of tweets we created based on ontological classes and degrees of harmful speech found in the text. We also propose supervised classification system for recognizing these respective harmful speech classes in the texts hence.