整合多类别仇恨言论数据集，用统一模型提升检测效果。
Improving Hate Speech Classification with Cross-Taxonomy Dataset Integration
- 构建通用分类体系，融合不同标注标准的数据集。
- 在独立测试集上表现优于单一分类器，提升检测准确率。
- 适合需要跨平台、多定义场景的有害内容检测应用。
算法化仇恨言论检测面临重大挑战，源于研究与实践中使用多样化的定义和数据集。社交媒体平台、法律框架及机构各自采用不同但重叠的定义，使分类工作复杂化。本研究通过证明现有数据集与分类体系可集成至统一模型，提升预测性能并减少对多个专用分类器的依赖。提出一种通用分类体系及可在单一框架内检测多种定义的仇恨言论分类器。通过结合两个广泛使用但标注方式不同的数据集进行验证，结果显示在独立测试集上分类性能得到提升。该工作凸显了数据集与分类体系整合在推进仇恨言论检测中的潜力，提高效率并增强跨场景适用性。
原文摘要 · Abstract (English)
Algorithmic hate speech detection faces significant challenges due to the diverse definitions and datasets used in research and practice. Social media platforms, legal frameworks, and institutions each apply distinct yet overlapping definitions, complicating classification efforts. This study addresses these challenges by demonstrating that existing datasets and taxonomies can be integrated into a unified model, enhancing prediction performance and reducing reliance on multiple specialized classifiers. The work introduces a universal taxonomy and a hate speech classifier capable of detecting a wide range of definitions within a single framework. Our approach is validated by combining two widely used but differently annotated datasets, showing improved classification performance on an independent test set. This work highlights the potential of dataset and taxonomy integration in advancing hate speech detection, increasing efficiency, and ensuring broader applicability across contexts.
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