Korean Online Hate Speech Dataset for Multilabel Classification: How Can Social Science Improve Dataset on Hate Speech?
Abstract
A 35K multilabel Korean hate speech dataset covering seven categories combines online comments, neutral Wikipedia sentences, human-in-the-loop labels, and rule-generated neutrals, improving LRAP from .892 to .919 and providing a culturally informed template for generalized hate speech dataset construction.
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.
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