标题： 英文文本的多标签分类机器学习模型和数据集综述 显示英文标题

标题： A Survey of Machine Learning Models and Datasets for the Multi-label Classification of Textual Hate Speech in English

摘要： 在线仇恨言论的传播可能对个人、在线社区和整个社会产生严重的负面后果。 这一点以及大量的仇恨在线内容促使实践者（即内容审核或执法部门）和研究人员对机器学习模型产生了兴趣，这些模型可以自动分类仇恨言论实例。 尽管大多数科研工作将仇恨言论分类作为二元任务来处理，但实践中常常需要根据目标、严重程度或合法性等对子类型进行区分，而这些子类型可能在个别内容中重叠。 因此，研究人员创建了数据集和机器学习模型，将文本数据中的仇恨言论分类作为多标签问题来处理。 本研究对这一新兴研究领域的英文科学文献进行了首次系统且全面的综述（N=46）。 我们提供了28个适合训练多标签分类模型的数据集的简明概述，结果显示在标签集、规模、元概念、标注过程和标注者间一致性方面存在显著的异质性。 我们对24篇提出合适分类模型的文献进行分析，进一步确认了评估的一致性不足，并倾向于基于双向编码器表示的变压器（BERT）和循环神经网络（RNNs）的架构。 我们识别出不平衡的训练数据、依赖众包平台、小而稀疏的数据集以及方法论的缺失对齐作为关键的开放问题，并提出了十项研究建议。 显示英文摘要

摘要： The dissemination of online hate speech can have serious negative consequences for individuals, online communities, and entire societies. This and the large volume of hateful online content prompted both practitioners', i.e., in content moderation or law enforcement, and researchers' interest in machine learning models to automatically classify instances of hate speech. Whereas most scientific works address hate speech classification as a binary task, practice often requires a differentiation into sub-types, e.g., according to target, severity, or legality, which may overlap for individual content. Hence, researchers created datasets and machine learning models that approach hate speech classification in textual data as a multi-label problem. This work presents the first systematic and comprehensive survey of scientific literature on this emerging research landscape in English (N=46). We contribute with a concise overview of 28 datasets suited for training multi-label classification models that reveals significant heterogeneity regarding label-set, size, meta-concept, annotation process, and inter-annotator agreement. Our analysis of 24 publications proposing suitable classification models further establishes inconsistency in evaluation and a preference for architectures based on Bidirectional Encoder Representation from Transformers (BERT) and Recurrent Neural Networks (RNNs). We identify imbalanced training data, reliance on crowdsourcing platforms, small and sparse datasets, and missing methodological alignment as critical open issues and formulate ten recommendations for research.