HateModerate: Testing Hate Speech Detectors against Content Moderation Policies

TL;DR

HateModerate is a new dataset designed to evaluate and improve hate speech detectors' alignment with social media content policies, revealing current models' shortcomings and enhancing their policy conformity.

Contribution

This work introduces HateModerate, a dataset for testing hate speech detectors against platform policies, and demonstrates how augmenting training data improves policy conformity.

Findings

State-of-the-art detectors often fail to conform to policies.

Augmenting training data improves policy adherence.

Models maintain original performance on standard tests.

Abstract

To protect users from massive hateful content, existing works studied automated hate speech detection. Despite the existing efforts, one question remains: do automated hate speech detectors conform to social media content policies? A platform's content policies are a checklist of content moderated by the social media platform. Because content moderation rules are often uniquely defined, existing hate speech datasets cannot directly answer this question. This work seeks to answer this question by creating HateModerate, a dataset for testing the behaviors of automated content moderators against content policies. First, we engage 28 annotators and GPT in a six-step annotation process, resulting in a list of hateful and non-hateful test suites matching each of Facebook's 41 hate speech policies. Second, we test the performance of state-of-the-art hate speech detectors against HateModerate, revealing substantial failures these models have in their conformity to the policies. Third, using HateModerate, we augment the training data of a top-downloaded hate detector on HuggingFace. We observe significant improvement in the models' conformity to content policies while having comparable scores on the original test data. Our dataset and code can be found in the attachment.