# Transfer Learning for Hate Speech Detection in Social Media

arXiv: https://arxiv.org/abs/1906.03829 · HTML: https://mlsanity.com/paper/1906.03829/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jun 2019.

## Built on by (most cited first, 13 of 13)

- [Generalizing Hate Speech Detection Using Multi-Task Learning: A Case Study of Political Public Figures](https://mlsanity.com/paper/2208.10598/) (2022)
- [Unsupervised Domain Adaptation for Hate Speech Detection Using a Data Augmentation Approach](https://mlsanity.com/paper/2107.12866/) (2021)
- [Leveraging cross-platform data to improve automated hate speech detection](https://mlsanity.com/paper/2102.04895/) (2021)
- [Contextual Hate Speech Detection in Code Mixed Text using Transformer Based Approaches](https://mlsanity.com/paper/2110.09338/) (2021)
- [Mario at EXIST 2025: A Simple Gateway to Effective Multilingual Sexism Detection](https://mlsanity.com/paper/2507.10996/) (2025)
- [LAHM : Large Annotated Dataset for Multi-Domain and Multilingual Hate Speech Identification](https://mlsanity.com/paper/2304.00913/) (2023)
- [X-Troll: eXplainable Detection of State-Sponsored Information Operations Agents](https://mlsanity.com/paper/2508.16021/) (2025)
- [Analysing Cyberbullying using Natural Language Processing by Understanding Jargon in Social Media](https://mlsanity.com/paper/2107.08902/) (2021)
- [Studying Socially Unacceptable Discourse Classification (SUD) through different eyes: "Are we on the same page ?"](https://mlsanity.com/paper/2308.04180/) (2023)
- [Improving Task Instructions for Data Annotators: How Clear Rules and Higher Pay Increase Performance in Data Annotation in the AI Economy](https://mlsanity.com/paper/2312.14565/) (2023)
- [Hate Speech Detection Using Cross-Platform Social Media Data In English and German Language](https://mlsanity.com/paper/2410.05287/) (2024)
- [Detecting Anti-Semitic Hate Speech using Transformer-based Large Language Models](https://mlsanity.com/paper/2405.03794/) (2024)
- [Defining, Understanding, and Detecting Online Toxicity: Challenges and Machine Learning Approaches](https://mlsanity.com/paper/2509.14264/) (2025)

## Abstract (arXiv)

Today, the internet is an integral part of our daily lives, enabling people to be more connected than ever before. However, this greater connectivity and access to information increase exposure to harmful content such as cyber-bullying and cyber-hatred. Models based on machine learning and natural language offer a way to make online platforms safer by identifying hate speech in web text autonomously. However, the main difficulty is annotating a sufficiently large number of examples to train these models. This paper uses a transfer learning technique to leverage two independent datasets jointly and builds a single representation of hate speech. We build an interpretable two-dimensional visualization tool of the constructed hate speech representation -- dubbed the Map of Hate -- in which multiple datasets can be projected and comparatively analyzed. The hateful content is annotated differently across the two datasets (racist and sexist in one dataset, hateful and offensive in another). However, the common representation successfully projects the harmless class of both datasets into the same space and can be used to uncover labeling errors (false positives). We also show that the joint representation boosts prediction performances when only a limited amount of supervision is available. These methods and insights hold the potential for safer social media and reduce the need to expose human moderators and annotators to distressing online messaging.