论文标题
韩国在线仇恨言论数据集用于多标签分类：社会科学如何改善仇恨言论的数据集？
Korean Online Hate Speech Dataset for Multilabel Classification: How Can Social Science Improve Dataset on Hate Speech?
论文作者
论文摘要
我们建议使用多标签的韩国在线仇恨言论数据集，其中涵盖了七类仇恨言论：（1）种族和国籍，（2）宗教，（3）区域主义，（4）年龄歧视，（5）厌女症，（6）性少数群体和（7）男性。我们的35K数据集由24K在线评论组成，Krippendorff的Alpha标签为.713，Wikipedia的2.2k中性句子，另外1.7K的标签句子由人类在循环过程中产生的句子和规则生成的7.1K中性句子产生。具有24K初始数据集的基本模型达到了LRAP .892的准确性，但在与11K附加数据结合使用后提高到.919。与传统的二进制仇恨和非仇恨二分法方法不同，我们设计了一个数据集，考虑了文化和语言背景，以克服基于西方文化的英语文本的局限性。因此，本文不仅限于呈现当地的仇恨言论数据集，而且还可以作为手册来构建具有基于社会科学观点的文化背景的更普遍的仇恨言论数据集。
We suggest a multilabel Korean online hate speech dataset that covers seven categories of hate speech: (1) Race and Nationality, (2) Religion, (3) Regionalism, (4) Ageism, (5) Misogyny, (6) Sexual Minorities, and (7) Male. Our 35K dataset consists of 24K online comments with Krippendorff's Alpha label accordance of .713, 2.2K neutral sentences from Wikipedia, 1.7K additionally labeled sentences generated by the Human-in-the-Loop procedure and rule-generated 7.1K neutral sentences. The base model with 24K initial dataset achieved the accuracy of LRAP .892, but improved to .919 after being combined with 11K additional data. Unlike the conventional binary hate and non-hate dichotomy approach, we designed a dataset considering both the cultural and linguistic context to overcome the limitations of western culture-based English texts. Thus, this paper is not only limited to presenting a local hate speech dataset but extends as a manual for building a more generalized hate speech dataset with diverse cultural backgrounds based on social science perspectives.
