# Improving Cross-Domain Hate Speech Generalizability with Emotion Knowledge

arXiv: https://arxiv.org/abs/2311.14865 · HTML: https://mlsanity.com/paper/2311.14865/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 45 references that are in the map.
- First posted on arXiv 24 Nov 2023.

## Builds on (most cited first, 19 of 19)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [An Overview of Multi-Task Learning in Deep Neural Networks](https://mlsanity.com/paper/1706.05098/) (2017)
- [GoEmotions: A Dataset of Fine-Grained Emotions](https://mlsanity.com/paper/2005.00547/) (2020)
- [HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection](https://mlsanity.com/paper/2012.10289/) (2020)
- [Predicting the Type and Target of Offensive Posts in Social Media](https://mlsanity.com/paper/1902.09666/) (2019)
- [Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior](https://mlsanity.com/paper/1802.00393/) (2018)
- [A Benchmark Dataset for Learning to Intervene in Online Hate Speech](https://mlsanity.com/paper/1909.04251/) (2019)
- [A Survey of Multi-task Learning in Natural Language Processing: Regarding Task Relatedness and Training Methods](https://mlsanity.com/paper/2204.03508/) (2022)
- [Large-Scale Hate Speech Detection with Cross-Domain Transfer](https://mlsanity.com/paper/2203.01111/) (2022)
- [FBERT: A Neural Transformer for Identifying Offensive Content](https://mlsanity.com/paper/2109.05074/) (2021)
- [How Hate Speech Varies by Target Identity: A Computational Analysis](https://mlsanity.com/paper/2210.10839/) (2022)
- [Improving Generalizability in Implicitly Abusive Language Detection with Concept Activation Vectors](https://mlsanity.com/paper/2204.02261/) (2022)
- [Joint Modelling of Emotion and Abusive Language Detection](https://mlsanity.com/paper/2005.14028/) (2020)
- [Fight Fire with Fire: Fine-tuning Hate Detectors using Large Samples of Generated Hate Speech](https://mlsanity.com/paper/2109.00591/) (2021)
- [Multi-Task Learning with Sentiment, Emotion, and Target Detection to Recognize Hate Speech and Offensive Language](https://mlsanity.com/paper/2109.10255/) (2021)
- [Attending the Emotions to Detect Online Abusive Language](https://mlsanity.com/paper/1909.03100/) (2019)
- ["It's Not Just Hate'': A Multi-Dimensional Perspective on Detecting Harmful Speech Online](https://mlsanity.com/paper/2210.15870/) (2022)
- [Hate Speech and Offensive Language Detection using an Emotion-aware Shared Encoder](https://mlsanity.com/paper/2302.08777/) (2023)

## Built on by (most cited first, 1 of 1)

- [Exploration and Evaluation of Bias in Cyberbullying Detection with Machine Learning](https://mlsanity.com/paper/2412.00609/) (2024)

## Abstract (arXiv)

Reliable automatic hate speech (HS) detection systems must adapt to the in-flow of diverse new data to curtail hate speech. However, hate speech detection systems commonly lack generalizability in identifying hate speech dissimilar to data used in training, impeding their robustness in real-world deployments. In this work, we propose a hate speech generalization framework that leverages emotion knowledge in a multitask architecture to improve the generalizability of hate speech detection in a cross-domain setting. We investigate emotion corpora with varying emotion categorical scopes to determine the best corpus scope for supplying emotion knowledge to foster generalized hate speech detection. We further assess the relationship between using pretrained Transformers models adapted for hate speech and its effect on our emotion-enriched hate speech generalization model. We perform extensive experiments on six publicly available datasets sourced from different online domains and show that our emotion-enriched HS detection generalization method demonstrates consistent generalization improvement in cross-domain evaluation, increasing generalization performance up to 18.1% and average cross-domain performance up to 8.5%, according to the F1 measure.