A Benchmark Dataset for Learning to Intervene in Online Hate Speech

本数据集名为‘A Benchmark Dataset for Learning to Intervene in Online Hate Speech’，由加州大学圣巴巴拉分校的研究团队创建。数据集包含从Reddit收集的5020个对话和从Gab收集的11825个对话，总计17020个对话。这些对话不仅标注了仇恨言论，还包含了由Mechanical Turk工作者编写的干预响应。数据集的创建旨在通过提供丰富的对话上下文和干预策略，帮助开发自动化的仇恨言论干预模型，以缓解在线仇恨言论的传播。

This benchmark dataset is titled *A Benchmark Dataset for Learning to Intervene in Online Hate Speech*, and was developed by a research team at the University of California, Santa Barbara. It comprises 5,020 conversations collected from Reddit and 11,825 conversations collected from Gab, amounting to a total of 17,020 conversations across both platforms. These conversations are not only annotated for hate speech content, but also feature intervention responses authored by Mechanical Turk workers. The dataset was constructed to support the development of automated hate speech intervention models by providing rich conversational contexts and diverse intervention strategies, thereby helping to alleviate the spread of online hate speech.