Multiclass English Hate Speech Dataset

Multiclass English Hate Speech Dataset is an extended and fine-grained version of the original binary-labelled hate-speech dataset released as part of the TweetEval benchmark (Hate sub-task). While the original dataset contained English posts annotated only as Hate or Non-Hate, this work substantially enhances it by applying a detailed manual re-annotation process to create multiple specific hate-speech categories. This provides richer granularity and enables more accurate modelling of real-world online hate.

All posts were manually reviewed and reclassified by trained annotators following a structured annotation guideline. The dataset introduces a comprehensive multiclass taxonomy capturing different forms of explicit and implicit hate, such as:

Gender-Based Hate Speech (Misogyny)

Gender-Based Hate Speech (Misandry)

Immigration & Xenophobic Hate Speech (Anti-Immigrant)

Immigration & Xenophobic Hate Speech (Anti-Refugee)

Immigration & Xenophobic Hate Speech (Xenophobia)

Through this re-annotation effort, the dataset transforms a simple binary classification problem into a 14-class fine-grained hate-speech categorization task, enabling more robust research on model sensitivity, bias analysis, safety evaluation, and explainability.

The dataset is suitable for:

Content-moderation research and safety evaluation

Sociolinguistic analysis of targeted abuse

All user identifiers and personally identifiable information (PII) have been removed or masked to ensure privacy and ethical compliance. The dataset includes the anonymized text, the newly assigned multiclass label, and mapping metadata to the original TweetEval record.

This resource aims to support researchers, practitioners, and policymakers in building safer and more responsible AI systems capable of detecting nuanced forms of online hate and targeted harassment.

多分类英语仇恨言论数据集（Multiclass English Hate Speech Dataset）是作为TweetEval基准测试（TweetEval benchmark）仇恨子任务发布的原始二元标注仇恨言论数据集的扩展细粒度版本。原始数据集仅将英语帖文标注为“仇恨言论”或“非仇恨言论”两类，本数据集通过细致的人工重标注流程构建了多个细分仇恨言论类别，对原始数据集进行了大幅增强，从而提供了更丰富的细粒度划分，能够更精准地对现实世界中的网络仇恨言论进行建模。

所有帖文均由经过培训的标注人员依照结构化标注指南进行人工审核与重新分类。本数据集构建了一套全面的多分类分类体系，涵盖显式与隐式仇恨言论的多种表现形式，具体包括：
- 基于性别的仇恨言论（厌女症，Misogyny）
- 基于性别的仇恨言论（厌男症，Misandry）
- 移民与仇外仇恨言论（反移民，Anti-Immigrant）
- 移民与仇外仇恨言论（反难民，Anti-Refugee）
- 移民与仇外仇恨言论（仇外主义，Xenophobia）

通过此次重标注工作，本数据集将原本简单的二元分类任务拓展为14类细粒度仇恨言论分类任务，为模型敏感性研究、偏差分析、安全性评估以及可解释性研究提供了更可靠的支撑。

本数据集适用于：
- 内容审核研究与安全性评估
- 针对性虐待行为的社会语言学分析

为保障隐私与符合伦理规范，所有用户标识符与个人可识别信息（personally identifiable information，PII）均已被移除或脱敏处理。本数据集包含脱敏后的文本、新分配的多分类标签，以及与原始TweetEval记录对应的映射元数据。

本数据集旨在为研究人员、从业者与政策制定者提供支撑，助力其构建能够识别细微网络仇恨言论与针对性骚扰行为的更安全、更负责任的人工智能系统。