# Examining a hate speech corpus for hate speech detection and popularity prediction

arXiv: https://arxiv.org/abs/1805.04661 · HTML: https://mlsanity.com/paper/1805.04661/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 May 2018.

## Built on by (most cited first, 10 of 10)

- [NLP for Counterspeech against Hate: A Survey and How-To Guide](https://mlsanity.com/paper/2403.20103/) (2024)
- [Abuse is Contextual, What about NLP? The Role of Context in Abusive Language Annotation and Detection](https://mlsanity.com/paper/2103.14916/) (2021)
- [Mitigating Racial Biases in Toxic Language Detection with an Equity-Based Ensemble Framework](https://mlsanity.com/paper/2109.13137/) (2021)
- [ToxiCraft: A Novel Framework for Synthetic Generation of Harmful Information](https://mlsanity.com/paper/2409.14740/) (2024)
- [PRODIGy: a PROfile-based DIalogue Generation dataset](https://mlsanity.com/paper/2311.05195/) (2023)
- [Countering Misinformation via Emotional Response Generation](https://mlsanity.com/paper/2311.10587/) (2023)
- [Putting Context in Context: the Impact of Discussion Structure on Text Classification](https://mlsanity.com/paper/2402.02975/) (2024)
- [Fine-tuning with HED-IT: The impact of human post-editing for dialogical language models](https://mlsanity.com/paper/2406.07288/) (2024)
- [A Target-Aware Analysis of Data Augmentation for Hate Speech Detection](https://mlsanity.com/paper/2410.08053/) (2024)
- [Before the Outrage: Challenges and Advances in Predicting Online Antisocial Behavior](https://mlsanity.com/paper/2507.20614/) (2025)

## Abstract (arXiv)

As research on hate speech becomes more and more relevant every day, most of it is still focused on hate speech detection. By attempting to replicate a hate speech detection experiment performed on an existing Twitter corpus annotated for hate speech, we highlight some issues that arise from doing research in the field of hate speech, which is essentially still in its infancy. We take a critical look at the training corpus in order to understand its biases, while also using it to venture beyond hate speech detection and investigate whether it can be used to shed light on other facets of research, such as popularity of hate tweets.