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.

我们提供了一个多标签的韩国在线仇恨言论数据集，考虑了文化和语言背景，建议了从社会科学角度构建更具多元文化背景的仇恨言论数据集的方法。基于 2.4 万的数据集测试模型，经过结合 1.1 万个额外数据，模型的 LRAP 准确率从 0.892 提高至 0.919。