Hate Speech According to the Law: An Analysis for Effective Detection

TL;DR

This paper explores the challenges of detecting prosecutable hate speech by analyzing legal frameworks and employing advanced language models, highlighting the importance of legal knowledge and annotations for effective classification.

Contribution

It introduces a novel approach combining legal expert opinions, pretrained models, and pseudo-labeling to improve hate speech detection aligned with legal standards.

Findings

Legal annotations aid in classifying prosecutable hate speech

Large language models can be adapted for legal hate speech detection

Differences in hate speech laws impact detection strategies

Abstract

The issue of hate speech extends beyond the confines of the online realm. It is a problem with real-life repercussions, prompting most nations to formulate legal frameworks that classify hate speech as a punishable offence. These legal frameworks differ from one country to another, contributing to the big chaos that online platforms have to face when addressing reported instances of hate speech. With the definitions of hate speech falling short in introducing a robust framework, we turn our gaze onto hate speech laws. We consult the opinion of legal experts on a hate speech dataset and we experiment by employing various approaches such as pretrained models both on hate speech and legal data, as well as exploiting two large language models (Qwen2-7B-Instruct and Meta-Llama-3-70B). Due to the time-consuming nature of data acquisition for prosecutable hate speech, we use pseudo-labeling to improve our pretrained models. This study highlights the importance of amplifying research on prosecutable hate speech and provides insights into effective strategies for combating hate speech within the parameters of legal frameworks. Our findings show that legal knowledge in the form of annotations can be useful when classifying prosecutable hate speech, yet more focus should be paid on the differences between the laws.